From 93c5af61ea800369de6fc657e8623e3f23e39f5f Mon Sep 17 00:00:00 2001 From: remigenet Date: Wed, 24 Jul 2024 16:44:06 +0200 Subject: [PATCH] update example --- README.md | 2 + examples/simple_example_tkan.ipynb | 4494 ++++++---------------------- 2 files changed, 869 insertions(+), 3627 deletions(-) diff --git a/README.md b/README.md index 103df83..9189f4d 100644 --- a/README.md +++ b/README.md @@ -6,6 +6,8 @@ The implementation is tested to be compatatible with Tensorflow, Jax and Torch. It is the original implementation of the [paper](https://arxiv.org/abs/2405.07344) The KAN part implementation has been inspired from [efficient_kan](https://github.com/Blealtan/efficient-kan), and is available [here](https://github.com/remigenet/keras_efficient_kan) and works similarly to it, thus not exactly like the [original implementation](https://github.com/KindXiaoming/pykan). +In case of performance consideration, the best setup tested used [jax docker image](https://hub.docker.com/r/bitnami/jax/) followed by installing jax using ```pip install "jax[cuda12]"```, this is what is used in the example section where you can compare the TKAN vs LSTM vs GRU time and performance. +I also discourage using as is the example for torch, it seems that currently when running test using torch backend with keras is much slower than torch directly, even for GRU or LSTM. ![TKAN representation](image/TKAN.drawio.png) diff --git a/examples/simple_example_tkan.ipynb b/examples/simple_example_tkan.ipynb index b75576d..7ccb161 100644 --- a/examples/simple_example_tkan.ipynb +++ b/examples/simple_example_tkan.ipynb @@ -5,113 +5,88 @@ "id": "bb186818-bd1d-46ed-a018-27efa013b206", "metadata": {}, "source": [ - "# " + "# TKAN example and comparison with benchmarks\n", + "\n", + "All test have been run on a RTX 4070 with an Core™ i7-6700K on vast.ai using this [jax docker image](https://hub.docker.com/r/bitnami/jax/)\n", + "\n", + "tkan version: 0.4.1" ] }, { "cell_type": "code", "execution_count": 1, "id": "bc3f1ac2-1785-4e08-89a5-0bc5e31ce2b7", - "metadata": {}, + "metadata": { + "scrolled": true + }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Requirement already satisfied: pandas in /usr/local/lib/python3.11/dist-packages (2.2.2)\n", - "Requirement already satisfied: numpy in /usr/local/lib/python3.11/dist-packages (1.26.4)\n", - "Requirement already satisfied: matplotlib in /usr/local/lib/python3.11/dist-packages (3.9.1)\n", - "Requirement already satisfied: pyarrow in /usr/local/lib/python3.11/dist-packages (17.0.0)\n", - "Requirement already satisfied: scikit-learn in /usr/local/lib/python3.11/dist-packages (1.5.1)\n", - "Requirement already satisfied: tkan in /usr/local/lib/python3.11/dist-packages (0.4.0)\n", - 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A possible replacement is to use pip for package installation. 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Use the --root-user-action option if you know what you are doing and want to suppress this warning.\u001b[0m\u001b[33m\n", "\u001b[0m" ] } ], "source": [ - "!pip install pandas numpy matplotlib pyarrow scikit-learn tkan " + "!pip install pandas numpy matplotlib pyarrow scikit-learn tkan \"jax[cuda12]\"" ] }, { "cell_type": "code", "execution_count": 2, - "id": "4bf2cb3c-56e6-49a6-91b8-47e616b850dc", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Requirement already satisfied: jax[cuda12] in /usr/local/lib/python3.11/dist-packages (0.4.30)\n", - "Requirement already satisfied: jaxlib<=0.4.30,>=0.4.27 in /usr/local/lib/python3.11/dist-packages (from jax[cuda12]) (0.4.30)\n", - "Requirement already satisfied: ml-dtypes>=0.2.0 in /usr/local/lib/python3.11/dist-packages (from jax[cuda12]) (0.4.0)\n", - "Requirement already satisfied: numpy>=1.22 in /usr/local/lib/python3.11/dist-packages (from jax[cuda12]) (1.26.4)\n", - "Requirement already satisfied: opt-einsum in /usr/local/lib/python3.11/dist-packages (from jax[cuda12]) (3.3.0)\n", - "Requirement already satisfied: scipy>=1.9 in /usr/local/lib/python3.11/dist-packages (from jax[cuda12]) (1.14.0)\n", - "Requirement already satisfied: jax-cuda12-plugin<=0.4.30,>=0.4.30 in /usr/local/lib/python3.11/dist-packages (from jax-cuda12-plugin[with_cuda]<=0.4.30,>=0.4.30; extra == \"cuda12\"->jax[cuda12]) (0.4.30)\n", - "Requirement already satisfied: jax-cuda12-pjrt==0.4.30 in /usr/local/lib/python3.11/dist-packages (from jax-cuda12-plugin<=0.4.30,>=0.4.30->jax-cuda12-plugin[with_cuda]<=0.4.30,>=0.4.30; extra == \"cuda12\"->jax[cuda12]) (0.4.30)\n", - "Requirement already satisfied: nvidia-cublas-cu12>=12.1.3.1 in /usr/local/lib/python3.11/dist-packages (from jax-cuda12-plugin[with_cuda]<=0.4.30,>=0.4.30; extra == \"cuda12\"->jax[cuda12]) (12.5.3.2)\n", - "Requirement already satisfied: nvidia-cuda-cupti-cu12>=12.1.105 in /usr/local/lib/python3.11/dist-packages (from jax-cuda12-plugin[with_cuda]<=0.4.30,>=0.4.30; extra == \"cuda12\"->jax[cuda12]) (12.5.82)\n", - "Requirement already satisfied: nvidia-cuda-nvcc-cu12>=12.1.105 in /usr/local/lib/python3.11/dist-packages (from jax-cuda12-plugin[with_cuda]<=0.4.30,>=0.4.30; extra == \"cuda12\"->jax[cuda12]) (12.5.82)\n", - "Requirement already satisfied: nvidia-cuda-runtime-cu12>=12.1.105 in /usr/local/lib/python3.11/dist-packages (from jax-cuda12-plugin[with_cuda]<=0.4.30,>=0.4.30; extra == \"cuda12\"->jax[cuda12]) (12.5.82)\n", - "Requirement already satisfied: nvidia-cudnn-cu12<10.0,>=9.0 in /usr/local/lib/python3.11/dist-packages (from jax-cuda12-plugin[with_cuda]<=0.4.30,>=0.4.30; extra == \"cuda12\"->jax[cuda12]) (9.2.1.18)\n", - "Requirement already satisfied: nvidia-cufft-cu12>=11.0.2.54 in /usr/local/lib/python3.11/dist-packages (from jax-cuda12-plugin[with_cuda]<=0.4.30,>=0.4.30; extra == \"cuda12\"->jax[cuda12]) (11.2.3.61)\n", - "Requirement already satisfied: nvidia-cusolver-cu12>=11.4.5.107 in /usr/local/lib/python3.11/dist-packages (from jax-cuda12-plugin[with_cuda]<=0.4.30,>=0.4.30; extra == \"cuda12\"->jax[cuda12]) (11.6.3.83)\n", - "Requirement already satisfied: nvidia-cusparse-cu12>=12.1.0.106 in /usr/local/lib/python3.11/dist-packages (from jax-cuda12-plugin[with_cuda]<=0.4.30,>=0.4.30; extra == \"cuda12\"->jax[cuda12]) (12.5.1.3)\n", - "Requirement already satisfied: nvidia-nccl-cu12>=2.18.1 in /usr/local/lib/python3.11/dist-packages (from jax-cuda12-plugin[with_cuda]<=0.4.30,>=0.4.30; extra == \"cuda12\"->jax[cuda12]) (2.22.3)\n", - "Requirement already satisfied: nvidia-nvjitlink-cu12>=12.1.105 in /usr/local/lib/python3.11/dist-packages (from jax-cuda12-plugin[with_cuda]<=0.4.30,>=0.4.30; extra == \"cuda12\"->jax[cuda12]) (12.5.82)\n", - "\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager, possibly rendering your system unusable.It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv. Use the --root-user-action option if you know what you are doing and want to suppress this warning.\u001b[0m\u001b[33m\n", - "\u001b[0m" - ] - } - ], - "source": [ - "!pip install -U \"jax[cuda12]\"" - ] - }, - { - "cell_type": "code", - "execution_count": 3, "id": "34213122-fabb-4d55-a918-a337be21b974", "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2024-07-23 18:15:27.842138: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:485] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n", - "2024-07-23 18:15:27.861965: E external/local_xla/xla/stream_executor/cuda/cuda_dnn.cc:8454] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n", - "2024-07-23 18:15:27.867851: E external/local_xla/xla/stream_executor/cuda/cuda_blas.cc:1452] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n" - ] - } - ], + "outputs": [], "source": [ "import os\n", "BACKEND = 'jax' # You can use any backend here \n", @@ -166,7 +141,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 3, "id": "735c5c88-924a-426e-b10f-a05e7b0556f2", "metadata": {}, "outputs": [ @@ -571,7 +546,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 4, "id": "34c9f03b-11bf-4c88-abcb-5fc8e43c6008", "metadata": {}, "outputs": [], @@ -694,17 +669,10 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 5, "id": "27b4f64a-08da-4cfc-97c9-1d5145887c23", "metadata": {}, "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2024-07-23 18:15:35.115393: W external/xla/xla/service/gpu/nvptx_compiler.cc:765] The NVIDIA driver's CUDA version is 12.4 which is older than the ptxas CUDA version (12.5.82). Because the driver is older than the ptxas version, XLA is disabling parallel compilation, which may slow down compilation. You should update your NVIDIA driver or use the NVIDIA-provided CUDA forward compatibility packages.\n" - ] - }, { "data": { "text/html": [ @@ -724,11 +692,11 @@ "
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n",
        "┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃\n",
        "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n",
-       "│ tkan (TKAN)                     │ (None, 75, 100)        │        41,750 │\n",
+       "│ tkan (TKAN)                     │ (None, 45, 100)        │        41,316 │\n",
        "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
        "│ tkan_1 (TKAN)                   │ (None, 100)            │        67,670 │\n",
        "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
-       "│ dense (Dense)                   │ (None, 15)             │         1,515 │\n",
+       "│ dense (Dense)                   │ (None, 1)              │           101 │\n",
        "└─────────────────────────────────┴────────────────────────┴───────────────┘\n",
        "
\n" ], @@ -736,11 +704,11 @@ "┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n", "┃\u001b[1m \u001b[0m\u001b[1mLayer (type) \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mOutput Shape \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m Param #\u001b[0m\u001b[1m \u001b[0m┃\n", "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n", - "│ tkan (\u001b[38;5;33mTKAN\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m75\u001b[0m, \u001b[38;5;34m100\u001b[0m) │ \u001b[38;5;34m41,750\u001b[0m │\n", + "│ tkan (\u001b[38;5;33mTKAN\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m45\u001b[0m, \u001b[38;5;34m100\u001b[0m) │ \u001b[38;5;34m41,316\u001b[0m │\n", "├─────────────────────────────────┼────────────────────────┼───────────────┤\n", "│ tkan_1 (\u001b[38;5;33mTKAN\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m100\u001b[0m) │ \u001b[38;5;34m67,670\u001b[0m │\n", "├─────────────────────────────────┼────────────────────────┼───────────────┤\n", - "│ dense (\u001b[38;5;33mDense\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m15\u001b[0m) │ \u001b[38;5;34m1,515\u001b[0m │\n", + "│ dense (\u001b[38;5;33mDense\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m) │ \u001b[38;5;34m101\u001b[0m │\n", "└─────────────────────────────────┴────────────────────────┴───────────────┘\n" ] }, @@ -750,11 +718,11 @@ { "data": { "text/html": [ - "
 Total params: 110,935 (433.34 KB)\n",
+       "
 Total params: 109,087 (426.12 KB)\n",
        "
\n"
       ],
       "text/plain": [
-       "\u001b[1m Total params: \u001b[0m\u001b[38;5;34m110,935\u001b[0m (433.34 KB)\n"
+       "\u001b[1m Total params: \u001b[0m\u001b[38;5;34m109,087\u001b[0m (426.12 KB)\n"
       ]
      },
      "metadata": {},
@@ -763,11 +731,11 @@
     {
      "data": {
       "text/html": [
-       "
 Trainable params: 110,915 (433.26 KB)\n",
+       "
 Trainable params: 109,067 (426.04 KB)\n",
        "
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       ],
       "text/plain": [
-       "\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m110,915\u001b[0m (433.26 KB)\n"
+       "\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m109,067\u001b[0m (426.04 KB)\n"
       ]
      },
      "metadata": {},
@@ -790,11 +758,16 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "124.26042032241821 0.09158093535460268\n",
-      "162.43516397476196 0.10293650922567563\n",
-      "165.25300359725952 0.09519336968632934\n",
-      "146.79839873313904 0.08672428978473426\n",
-      "145.06835103034973 0.09001762052527772\n"
+      "87.7924542427063 0.2883694212812108\n",
+      "58.81150460243225 0.2996397661396135\n",
+      "93.38526654243469 0.3341084410571994\n",
+      "69.46869540214539 0.31051983221133617\n",
+      "68.03620338439941 0.2921106524335578\n",
+      "69.86754989624023 0.31632189994316484\n",
+      "83.32379245758057 0.3110331089568017\n",
+      "87.04948496818542 0.32794331666897736\n",
+      "84.89363670349121 0.31305216100227273\n",
+      "65.98418259620667 0.30768323851050017\n"
      ]
     },
     {
@@ -816,11 +789,11 @@
        "
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n",
        "┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃\n",
        "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n",
-       "│ gru (GRU)                       │ (None, 75, 100)        │        36,300 │\n",
+       "│ gru (GRU)                       │ (None, 45, 100)        │        36,300 │\n",
        "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
        "│ gru_1 (GRU)                     │ (None, 100)            │        60,600 │\n",
        "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
-       "│ dense_5 (Dense)                 │ (None, 15)             │         1,515 │\n",
+       "│ dense_10 (Dense)                │ (None, 1)              │           101 │\n",
        "└─────────────────────────────────┴────────────────────────┴───────────────┘\n",
        "
\n"
       ],
@@ -828,11 +801,11 @@
        "┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n",
        "┃\u001b[1m \u001b[0m\u001b[1mLayer (type)                   \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mOutput Shape          \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m      Param #\u001b[0m\u001b[1m \u001b[0m┃\n",
        "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n",
-       "│ gru (\u001b[38;5;33mGRU\u001b[0m)                       │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m75\u001b[0m, \u001b[38;5;34m100\u001b[0m)        │        \u001b[38;5;34m36,300\u001b[0m │\n",
+       "│ gru (\u001b[38;5;33mGRU\u001b[0m)                       │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m45\u001b[0m, \u001b[38;5;34m100\u001b[0m)        │        \u001b[38;5;34m36,300\u001b[0m │\n",
        "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
        "│ gru_1 (\u001b[38;5;33mGRU\u001b[0m)                     │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m100\u001b[0m)            │        \u001b[38;5;34m60,600\u001b[0m │\n",
        "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
-       "│ dense_5 (\u001b[38;5;33mDense\u001b[0m)                 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m15\u001b[0m)             │         \u001b[38;5;34m1,515\u001b[0m │\n",
+       "│ dense_10 (\u001b[38;5;33mDense\u001b[0m)                │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m)              │           \u001b[38;5;34m101\u001b[0m │\n",
        "└─────────────────────────────────┴────────────────────────┴───────────────┘\n"
       ]
      },
@@ -842,11 +815,11 @@
     {
      "data": {
       "text/html": [
-       "
 Total params: 98,415 (384.43 KB)\n",
+       "
 Total params: 97,001 (378.91 KB)\n",
        "
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       ],
       "text/plain": [
-       "\u001b[1m Total params: \u001b[0m\u001b[38;5;34m98,415\u001b[0m (384.43 KB)\n"
+       "\u001b[1m Total params: \u001b[0m\u001b[38;5;34m97,001\u001b[0m (378.91 KB)\n"
       ]
      },
      "metadata": {},
@@ -855,11 +828,11 @@
     {
      "data": {
       "text/html": [
-       "
 Trainable params: 98,415 (384.43 KB)\n",
+       "
 Trainable params: 97,001 (378.91 KB)\n",
        "
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       ],
       "text/plain": [
-       "\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m98,415\u001b[0m (384.43 KB)\n"
+       "\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m97,001\u001b[0m (378.91 KB)\n"
       ]
      },
      "metadata": {},
@@ -882,11 +855,16 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "40.458553075790405 0.09533817072522596\n",
-      "41.53169059753418 0.06848331293892884\n",
-      "41.19035625457764 0.08615624519190934\n",
-      "47.83834481239319 0.05216959504533443\n",
-      "41.37253522872925 0.09000230869662042\n"
+      "25.800300121307373 0.39845659417931767\n",
+      "33.090811014175415 0.3765754299721896\n",
+      "29.157642126083374 0.3996904390926539\n",
+      "20.830080032348633 0.39383111789434266\n",
+      "24.594220638275146 0.39854787088819454\n",
+      "25.829734086990356 0.3980608345819734\n",
+      "20.8519549369812 0.39818872394114213\n",
+      "29.55962371826172 0.39876597794912183\n",
+      "28.695900917053223 0.4014753210019817\n",
+      "31.473294496536255 0.385033271890177\n"
      ]
     },
     {
@@ -908,11 +886,11 @@
        "
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n",
        "┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃\n",
        "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n",
-       "│ lstm (LSTM)                     │ (None, 75, 100)        │        48,000 │\n",
+       "│ lstm (LSTM)                     │ (None, 45, 100)        │        48,000 │\n",
        "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
        "│ lstm_1 (LSTM)                   │ (None, 100)            │        80,400 │\n",
        "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
-       "│ dense_10 (Dense)                │ (None, 15)             │         1,515 │\n",
+       "│ dense_20 (Dense)                │ (None, 1)              │           101 │\n",
        "└─────────────────────────────────┴────────────────────────┴───────────────┘\n",
        "
\n"
       ],
@@ -920,11 +898,11 @@
        "┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n",
        "┃\u001b[1m \u001b[0m\u001b[1mLayer (type)                   \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mOutput Shape          \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m      Param #\u001b[0m\u001b[1m \u001b[0m┃\n",
        "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n",
-       "│ lstm (\u001b[38;5;33mLSTM\u001b[0m)                     │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m75\u001b[0m, \u001b[38;5;34m100\u001b[0m)        │        \u001b[38;5;34m48,000\u001b[0m │\n",
+       "│ lstm (\u001b[38;5;33mLSTM\u001b[0m)                     │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m45\u001b[0m, \u001b[38;5;34m100\u001b[0m)        │        \u001b[38;5;34m48,000\u001b[0m │\n",
        "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
        "│ lstm_1 (\u001b[38;5;33mLSTM\u001b[0m)                   │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m100\u001b[0m)            │        \u001b[38;5;34m80,400\u001b[0m │\n",
        "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
-       "│ dense_10 (\u001b[38;5;33mDense\u001b[0m)                │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m15\u001b[0m)             │         \u001b[38;5;34m1,515\u001b[0m │\n",
+       "│ dense_20 (\u001b[38;5;33mDense\u001b[0m)                │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m)              │           \u001b[38;5;34m101\u001b[0m │\n",
        "└─────────────────────────────────┴────────────────────────┴───────────────┘\n"
       ]
      },
@@ -934,11 +912,11 @@
     {
      "data": {
       "text/html": [
-       "
 Total params: 129,915 (507.48 KB)\n",
+       "
 Total params: 128,501 (501.96 KB)\n",
        "
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       ],
       "text/plain": [
-       "\u001b[1m Total params: \u001b[0m\u001b[38;5;34m129,915\u001b[0m (507.48 KB)\n"
+       "\u001b[1m Total params: \u001b[0m\u001b[38;5;34m128,501\u001b[0m (501.96 KB)\n"
       ]
      },
      "metadata": {},
@@ -947,11 +925,11 @@
     {
      "data": {
       "text/html": [
-       "
 Trainable params: 129,915 (507.48 KB)\n",
+       "
 Trainable params: 128,501 (501.96 KB)\n",
        "
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       ],
       "text/plain": [
-       "\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m129,915\u001b[0m (507.48 KB)\n"
+       "\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m128,501\u001b[0m (501.96 KB)\n"
       ]
      },
      "metadata": {},
@@ -974,583 +952,220 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "34.312363147735596 -0.19760337708902084\n",
-      "31.26186227798462 -0.17731950497927035\n",
-      "34.37222385406494 -0.37332071192906224\n",
-      "35.09816241264343 -0.08747675367134147\n",
-      "33.26098704338074 -0.3229705947886879\n",
-      "R2 scores\n",
-      "Means:\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/usr/local/lib/python3.11/dist-packages/numpy/core/fromnumeric.py:3504: RuntimeWarning: Mean of empty slice.\n",
-      "  return _methods._mean(a, axis=axis, dtype=dtype,\n",
-      "/usr/local/lib/python3.11/dist-packages/numpy/core/_methods.py:129: RuntimeWarning: invalid value encountered in scalar divide\n",
-      "  ret = ret.dtype.type(ret / rcount)\n"
+      "17.663942098617554 0.3868841900601451\n",
+      "18.93087387084961 0.36429593525523807\n",
+      "17.406195163726807 0.3900695412878702\n",
+      "20.44808530807495 0.3786097961170115\n",
+      "16.755755186080933 0.392573888221295\n",
+      "20.213451862335205 0.3840845450430137\n",
+      "22.768882513046265 0.36630601832303555\n",
+      "18.173134088516235 0.3832138605836025\n",
+      "17.591335773468018 0.3918248620808171\n",
+      "19.007630586624146 0.37993474576853103\n"
      ]
     },
     {
      "data": {
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-       "
TKANGRULSTM
150.0932910.07843-0.231738
12NaNNaNNaN
9NaNNaNNaN
6NaNNaNNaN
3NaNNaNNaN
1NaNNaNNaN
\n",
-       "
"
+       "
Model: \"TKAN\"\n",
+       "
\n"
       ],
       "text/plain": [
-       "        TKAN      GRU      LSTM\n",
-       "15  0.093291  0.07843 -0.231738\n",
-       "12       NaN      NaN       NaN\n",
-       "9        NaN      NaN       NaN\n",
-       "6        NaN      NaN       NaN\n",
-       "3        NaN      NaN       NaN\n",
-       "1        NaN      NaN       NaN"
+       "\u001b[1mModel: \"TKAN\"\u001b[0m\n"
       ]
      },
      "metadata": {},
      "output_type": "display_data"
     },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/usr/local/lib/python3.11/dist-packages/numpy/core/fromnumeric.py:3504: RuntimeWarning: Mean of empty slice.\n",
-      "  return _methods._mean(a, axis=axis, dtype=dtype,\n",
-      "/usr/local/lib/python3.11/dist-packages/numpy/core/_methods.py:129: RuntimeWarning: invalid value encountered in scalar divide\n",
-      "  ret = ret.dtype.type(ret / rcount)\n"
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TKANGRULSTM
150.0677050.0681940.078686
12NaNNaNNaN
9NaNNaNNaN
6NaNNaNNaN
3NaNNaNNaN
1NaNNaNNaN
\n",
-       "
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+       "
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n",
+       "┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃\n",
+       "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n",
+       "│ tkan_20 (TKAN)                  │ (None, 45, 100)        │        41,316 │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ tkan_21 (TKAN)                  │ (None, 100)            │        67,670 │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ dense_30 (Dense)                │ (None, 3)              │           303 │\n",
+       "└─────────────────────────────────┴────────────────────────┴───────────────┘\n",
+       "
\n"
       ],
       "text/plain": [
-       "        TKAN       GRU      LSTM\n",
-       "15  0.067705  0.068194  0.078686\n",
-       "12       NaN       NaN       NaN\n",
-       "9        NaN       NaN       NaN\n",
-       "6        NaN       NaN       NaN\n",
-       "3        NaN       NaN       NaN\n",
-       "1        NaN       NaN       NaN"
+       "┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n",
+       "┃\u001b[1m \u001b[0m\u001b[1mLayer (type)                   \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mOutput Shape          \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m      Param #\u001b[0m\u001b[1m \u001b[0m┃\n",
+       "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n",
+       "│ tkan_20 (\u001b[38;5;33mTKAN\u001b[0m)                  │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m45\u001b[0m, \u001b[38;5;34m100\u001b[0m)        │        \u001b[38;5;34m41,316\u001b[0m │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ tkan_21 (\u001b[38;5;33mTKAN\u001b[0m)                  │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m100\u001b[0m)            │        \u001b[38;5;34m67,670\u001b[0m │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ dense_30 (\u001b[38;5;33mDense\u001b[0m)                │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m3\u001b[0m)              │           \u001b[38;5;34m303\u001b[0m │\n",
+       "└─────────────────────────────────┴────────────────────────┴───────────────┘\n"
       ]
      },
      "metadata": {},
      "output_type": "display_data"
     },
     {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Std:\n"
-     ]
+     "data": {
+      "text/html": [
+       "
 Total params: 109,289 (426.91 KB)\n",
+       "
\n"
+      ],
+      "text/plain": [
+       "\u001b[1m Total params: \u001b[0m\u001b[38;5;34m109,289\u001b[0m (426.91 KB)\n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
     },
     {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/usr/local/lib/python3.11/dist-packages/numpy/core/_methods.py:206: RuntimeWarning: Degrees of freedom <= 0 for slice\n",
-      "  ret = _var(a, axis=axis, dtype=dtype, out=out, ddof=ddof,\n",
-      "/usr/local/lib/python3.11/dist-packages/numpy/core/_methods.py:163: RuntimeWarning: invalid value encountered in divide\n",
-      "  arrmean = um.true_divide(arrmean, div, out=arrmean,\n",
-      "/usr/local/lib/python3.11/dist-packages/numpy/core/_methods.py:198: RuntimeWarning: invalid value encountered in scalar divide\n",
-      "  ret = ret.dtype.type(ret / rcount)\n"
-     ]
+     "data": {
+      "text/html": [
+       "
 Trainable params: 109,269 (426.83 KB)\n",
+       "
\n"
+      ],
+      "text/plain": [
+       "\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m109,269\u001b[0m (426.83 KB)\n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
     },
     {
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TKANGRULSTM
150.0055390.0159250.103254
12NaNNaNNaN
9NaNNaNNaN
6NaNNaNNaN
3NaNNaNNaN
1NaNNaNNaN
\n",
-       "
"
+       "
 Non-trainable params: 20 (80.00 B)\n",
+       "
\n"
       ],
       "text/plain": [
-       "        TKAN       GRU      LSTM\n",
-       "15  0.005539  0.015925  0.103254\n",
-       "12       NaN       NaN       NaN\n",
-       "9        NaN       NaN       NaN\n",
-       "6        NaN       NaN       NaN\n",
-       "3        NaN       NaN       NaN\n",
-       "1        NaN       NaN       NaN"
+       "\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m20\u001b[0m (80.00 B)\n"
       ]
      },
      "metadata": {},
      "output_type": "display_data"
     },
     {
-     "name": "stderr",
+     "name": "stdout",
      "output_type": "stream",
      "text": [
-      "/usr/local/lib/python3.11/dist-packages/numpy/core/_methods.py:206: RuntimeWarning: Degrees of freedom <= 0 for slice\n",
-      "  ret = _var(a, axis=axis, dtype=dtype, out=out, ddof=ddof,\n",
-      "/usr/local/lib/python3.11/dist-packages/numpy/core/_methods.py:163: RuntimeWarning: invalid value encountered in divide\n",
-      "  arrmean = um.true_divide(arrmean, div, out=arrmean,\n",
-      "/usr/local/lib/python3.11/dist-packages/numpy/core/_methods.py:198: RuntimeWarning: invalid value encountered in scalar divide\n",
-      "  ret = ret.dtype.type(ret / rcount)\n"
+      "96.62368559837341 0.2039716435474497\n",
+      "74.19592547416687 0.1932276162314687\n",
+      "79.30283999443054 0.186078923268888\n",
+      "67.78045701980591 0.1888747778655048\n",
+      "90.85375475883484 0.20038166436812602\n",
+      "83.50374150276184 0.198828800729096\n",
+      "79.80795526504517 0.1848691783440326\n",
+      "59.527796268463135 0.18538958325425756\n",
+      "73.05465388298035 0.17813436933288326\n",
+      "77.17678189277649 0.185736715040992\n"
      ]
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TKANGRULSTM
150.0002150.0005780.003284
12NaNNaNNaN
9NaNNaNNaN
6NaNNaNNaN
3NaNNaNNaN
1NaNNaNNaN
\n",
-       "
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+       "
Model: \"GRU\"\n",
+       "
\n"
       ],
       "text/plain": [
-       "        TKAN       GRU      LSTM\n",
-       "15  0.000215  0.000578  0.003284\n",
-       "12       NaN       NaN       NaN\n",
-       "9        NaN       NaN       NaN\n",
-       "6        NaN       NaN       NaN\n",
-       "3        NaN       NaN       NaN\n",
-       "1        NaN       NaN       NaN"
+       "\u001b[1mModel: \"GRU\"\u001b[0m\n"
       ]
      },
      "metadata": {},
      "output_type": "display_data"
     },
     {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Training Times\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/usr/local/lib/python3.11/dist-packages/numpy/core/fromnumeric.py:3504: RuntimeWarning: Mean of empty slice.\n",
-      "  return _methods._mean(a, axis=axis, dtype=dtype,\n",
-      "/usr/local/lib/python3.11/dist-packages/numpy/core/_methods.py:129: RuntimeWarning: invalid value encountered in scalar divide\n",
-      "  ret = ret.dtype.type(ret / rcount)\n"
-     ]
+     "data": {
+      "text/html": [
+       "
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n",
+       "┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃\n",
+       "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n",
+       "│ gru_20 (GRU)                    │ (None, 45, 100)        │        36,300 │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ gru_21 (GRU)                    │ (None, 100)            │        60,600 │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ dense_40 (Dense)                │ (None, 3)              │           303 │\n",
+       "└─────────────────────────────────┴────────────────────────┴───────────────┘\n",
+       "
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+      ],
+      "text/plain": [
+       "┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n",
+       "┃\u001b[1m \u001b[0m\u001b[1mLayer (type)                   \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mOutput Shape          \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m      Param #\u001b[0m\u001b[1m \u001b[0m┃\n",
+       "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n",
+       "│ gru_20 (\u001b[38;5;33mGRU\u001b[0m)                    │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m45\u001b[0m, \u001b[38;5;34m100\u001b[0m)        │        \u001b[38;5;34m36,300\u001b[0m │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ gru_21 (\u001b[38;5;33mGRU\u001b[0m)                    │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m100\u001b[0m)            │        \u001b[38;5;34m60,600\u001b[0m │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ dense_40 (\u001b[38;5;33mDense\u001b[0m)                │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m3\u001b[0m)              │           \u001b[38;5;34m303\u001b[0m │\n",
+       "└─────────────────────────────────┴────────────────────────┴───────────────┘\n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
     },
     {
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TKANGRULSTM
15148.76306842.47829633.66112
12NaNNaNNaN
9NaNNaNNaN
6NaNNaNNaN
3NaNNaNNaN
1NaNNaNNaN
\n",
-       "
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+       "
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+       "
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       ],
       "text/plain": [
-       "          TKAN        GRU      LSTM\n",
-       "15  148.763068  42.478296  33.66112\n",
-       "12         NaN        NaN       NaN\n",
-       "9          NaN        NaN       NaN\n",
-       "6          NaN        NaN       NaN\n",
-       "3          NaN        NaN       NaN\n",
-       "1          NaN        NaN       NaN"
+       "\u001b[1m Total params: \u001b[0m\u001b[38;5;34m97,203\u001b[0m (379.70 KB)\n"
       ]
      },
      "metadata": {},
      "output_type": "display_data"
     },
     {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/usr/local/lib/python3.11/dist-packages/numpy/core/_methods.py:206: RuntimeWarning: Degrees of freedom <= 0 for slice\n",
-      "  ret = _var(a, axis=axis, dtype=dtype, out=out, ddof=ddof,\n",
-      "/usr/local/lib/python3.11/dist-packages/numpy/core/_methods.py:163: RuntimeWarning: invalid value encountered in divide\n",
-      "  arrmean = um.true_divide(arrmean, div, out=arrmean,\n",
-      "/usr/local/lib/python3.11/dist-packages/numpy/core/_methods.py:198: RuntimeWarning: invalid value encountered in scalar divide\n",
-      "  ret = ret.dtype.type(ret / rcount)\n"
-     ]
+     "data": {
+      "text/html": [
+       "
 Trainable params: 97,203 (379.70 KB)\n",
+       "
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+      ],
+      "text/plain": [
+       "\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m97,203\u001b[0m (379.70 KB)\n"
+      ]
+     },
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+     "output_type": "display_data"
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      "data": {
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TKANGRULSTM
1514.6747052.7050711.335022
12NaNNaNNaN
9NaNNaNNaN
6NaNNaNNaN
3NaNNaNNaN
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\n",
-       "
"
+       "
 Non-trainable params: 0 (0.00 B)\n",
+       "
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       ],
       "text/plain": [
-       "         TKAN       GRU      LSTM\n",
-       "15  14.674705  2.705071  1.335022\n",
-       "12        NaN       NaN       NaN\n",
-       "9         NaN       NaN       NaN\n",
-       "6         NaN       NaN       NaN\n",
-       "3         NaN       NaN       NaN\n",
-       "1         NaN       NaN       NaN"
+       "\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m0\u001b[0m (0.00 B)\n"
       ]
      },
      "metadata": {},
      "output_type": "display_data"
     },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "32.23410701751709 0.24445524304420677\n",
+      "34.76943516731262 0.2201955661466759\n",
+      "25.166553735733032 0.23882125234574236\n",
+      "28.084814310073853 0.2344194906558783\n",
+      "27.79819416999817 0.235279604982632\n",
+      "31.362367868423462 0.23122380522590277\n",
+      "24.73177719116211 0.2426694875126042\n",
+      "26.578373432159424 0.23963082071966313\n",
+      "27.062561988830566 0.24032075216240234\n",
+      "28.228745222091675 0.24178121308010916\n"
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     {
      "data": {
       "text/html": [
-       "
Model: \"TKAN\"\n",
+       "
Model: \"LSTM\"\n",
        "
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       ],
       "text/plain": [
-       "\u001b[1mModel: \"TKAN\"\u001b[0m\n"
+       "\u001b[1mModel: \"LSTM\"\u001b[0m\n"
       ]
      },
      "metadata": {},
@@ -1562,11 +1177,11 @@
        "
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n",
        "┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃\n",
        "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n",
-       "│ tkan_10 (TKAN)                  │ (None, 60, 100)        │        41,750 │\n",
+       "│ lstm_20 (LSTM)                  │ (None, 45, 100)        │        48,000 │\n",
        "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
-       "│ tkan_11 (TKAN)                  │ (None, 100)            │        67,670 │\n",
+       "│ lstm_21 (LSTM)                  │ (None, 100)            │        80,400 │\n",
        "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
-       "│ dense_15 (Dense)                │ (None, 12)             │         1,212 │\n",
+       "│ dense_50 (Dense)                │ (None, 3)              │           303 │\n",
        "└─────────────────────────────────┴────────────────────────┴───────────────┘\n",
        "
\n"
       ],
@@ -1574,11 +1189,11 @@
        "┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n",
        "┃\u001b[1m \u001b[0m\u001b[1mLayer (type)                   \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mOutput Shape          \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m      Param #\u001b[0m\u001b[1m \u001b[0m┃\n",
        "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n",
-       "│ tkan_10 (\u001b[38;5;33mTKAN\u001b[0m)                  │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m60\u001b[0m, \u001b[38;5;34m100\u001b[0m)        │        \u001b[38;5;34m41,750\u001b[0m │\n",
+       "│ lstm_20 (\u001b[38;5;33mLSTM\u001b[0m)                  │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m45\u001b[0m, \u001b[38;5;34m100\u001b[0m)        │        \u001b[38;5;34m48,000\u001b[0m │\n",
        "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
-       "│ tkan_11 (\u001b[38;5;33mTKAN\u001b[0m)                  │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m100\u001b[0m)            │        \u001b[38;5;34m67,670\u001b[0m │\n",
+       "│ lstm_21 (\u001b[38;5;33mLSTM\u001b[0m)                  │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m100\u001b[0m)            │        \u001b[38;5;34m80,400\u001b[0m │\n",
        "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
-       "│ dense_15 (\u001b[38;5;33mDense\u001b[0m)                │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m12\u001b[0m)             │         \u001b[38;5;34m1,212\u001b[0m │\n",
+       "│ dense_50 (\u001b[38;5;33mDense\u001b[0m)                │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m3\u001b[0m)              │           \u001b[38;5;34m303\u001b[0m │\n",
        "└─────────────────────────────────┴────────────────────────┴───────────────┘\n"
       ]
      },
@@ -1588,11 +1203,11 @@
     {
      "data": {
       "text/html": [
-       "
 Total params: 110,632 (432.16 KB)\n",
+       "
 Total params: 128,703 (502.75 KB)\n",
        "
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       ],
       "text/plain": [
-       "\u001b[1m Total params: \u001b[0m\u001b[38;5;34m110,632\u001b[0m (432.16 KB)\n"
+       "\u001b[1m Total params: \u001b[0m\u001b[38;5;34m128,703\u001b[0m (502.75 KB)\n"
       ]
      },
      "metadata": {},
@@ -1601,11 +1216,11 @@
     {
      "data": {
       "text/html": [
-       "
 Trainable params: 110,612 (432.08 KB)\n",
+       "
 Trainable params: 128,703 (502.75 KB)\n",
        "
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       ],
       "text/plain": [
-       "\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m110,612\u001b[0m (432.08 KB)\n"
+       "\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m128,703\u001b[0m (502.75 KB)\n"
       ]
      },
      "metadata": {},
@@ -1614,11 +1229,11 @@
     {
      "data": {
       "text/html": [
-       "
 Non-trainable params: 20 (80.00 B)\n",
+       "
 Non-trainable params: 0 (0.00 B)\n",
        "
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       "text/plain": [
-       "\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m20\u001b[0m (80.00 B)\n"
+       "\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m0\u001b[0m (0.00 B)\n"
       ]
      },
      "metadata": {},
@@ -1628,21 +1243,26 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "131.22357177734375 0.09087923067578507\n",
-      "141.15374612808228 0.10076559080540619\n",
-      "121.34675312042236 0.09357512681452108\n",
-      "129.00918769836426 0.10084381545148026\n",
-      "135.5328950881958 0.09396223859149976\n"
+      "20.623400688171387 0.14482767971630928\n",
+      "21.055927991867065 0.07395821838488396\n",
+      "24.374985218048096 0.022805938949401933\n",
+      "21.713353157043457 0.0749404329796987\n",
+      "21.184755563735962 0.1522887184364525\n",
+      "18.015154600143433 0.16326070883053923\n",
+      "18.17797017097473 0.1804372350265144\n",
+      "17.993833541870117 0.18266789728362973\n",
+      "18.728025197982788 0.14342017896777437\n",
+      "20.97398853302002 -0.002893617498768264\n"
      ]
     },
     {
      "data": {
       "text/html": [
-       "
Model: \"GRU\"\n",
+       "
Model: \"TKAN\"\n",
        "
\n"
       ],
       "text/plain": [
-       "\u001b[1mModel: \"GRU\"\u001b[0m\n"
+       "\u001b[1mModel: \"TKAN\"\u001b[0m\n"
       ]
      },
      "metadata": {},
@@ -1654,11 +1274,11 @@
        "
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n",
        "┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃\n",
        "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n",
-       "│ gru_10 (GRU)                    │ (None, 60, 100)        │        36,300 │\n",
+       "│ tkan_40 (TKAN)                  │ (None, 45, 100)        │        41,316 │\n",
        "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
-       "│ gru_11 (GRU)                    │ (None, 100)            │        60,600 │\n",
+       "│ tkan_41 (TKAN)                  │ (None, 100)            │        67,670 │\n",
        "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
-       "│ dense_20 (Dense)                │ (None, 12)             │         1,212 │\n",
+       "│ dense_60 (Dense)                │ (None, 6)              │           606 │\n",
        "└─────────────────────────────────┴────────────────────────┴───────────────┘\n",
        "
\n"
       ],
@@ -1666,11 +1286,11 @@
        "┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n",
        "┃\u001b[1m \u001b[0m\u001b[1mLayer (type)                   \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mOutput Shape          \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m      Param #\u001b[0m\u001b[1m \u001b[0m┃\n",
        "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n",
-       "│ gru_10 (\u001b[38;5;33mGRU\u001b[0m)                    │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m60\u001b[0m, \u001b[38;5;34m100\u001b[0m)        │        \u001b[38;5;34m36,300\u001b[0m │\n",
+       "│ tkan_40 (\u001b[38;5;33mTKAN\u001b[0m)                  │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m45\u001b[0m, \u001b[38;5;34m100\u001b[0m)        │        \u001b[38;5;34m41,316\u001b[0m │\n",
        "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
-       "│ gru_11 (\u001b[38;5;33mGRU\u001b[0m)                    │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m100\u001b[0m)            │        \u001b[38;5;34m60,600\u001b[0m │\n",
+       "│ tkan_41 (\u001b[38;5;33mTKAN\u001b[0m)                  │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m100\u001b[0m)            │        \u001b[38;5;34m67,670\u001b[0m │\n",
        "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
-       "│ dense_20 (\u001b[38;5;33mDense\u001b[0m)                │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m12\u001b[0m)             │         \u001b[38;5;34m1,212\u001b[0m │\n",
+       "│ dense_60 (\u001b[38;5;33mDense\u001b[0m)                │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m6\u001b[0m)              │           \u001b[38;5;34m606\u001b[0m │\n",
        "└─────────────────────────────────┴────────────────────────┴───────────────┘\n"
       ]
      },
@@ -1680,11 +1300,11 @@
     {
      "data": {
       "text/html": [
-       "
 Total params: 98,112 (383.25 KB)\n",
+       "
 Total params: 109,592 (428.09 KB)\n",
        "
\n"
       ],
       "text/plain": [
-       "\u001b[1m Total params: \u001b[0m\u001b[38;5;34m98,112\u001b[0m (383.25 KB)\n"
+       "\u001b[1m Total params: \u001b[0m\u001b[38;5;34m109,592\u001b[0m (428.09 KB)\n"
       ]
      },
      "metadata": {},
@@ -1693,11 +1313,11 @@
     {
      "data": {
       "text/html": [
-       "
 Trainable params: 98,112 (383.25 KB)\n",
+       "
 Trainable params: 109,572 (428.02 KB)\n",
        "
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       ],
       "text/plain": [
-       "\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m98,112\u001b[0m (383.25 KB)\n"
+       "\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m109,572\u001b[0m (428.02 KB)\n"
       ]
      },
      "metadata": {},
@@ -1706,11 +1326,11 @@
     {
      "data": {
       "text/html": [
-       "
 Non-trainable params: 0 (0.00 B)\n",
+       "
 Non-trainable params: 20 (80.00 B)\n",
        "
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       "text/plain": [
-       "\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m0\u001b[0m (0.00 B)\n"
+       "\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m20\u001b[0m (80.00 B)\n"
       ]
      },
      "metadata": {},
@@ -1720,21 +1340,26 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "38.70786952972412 0.08947185062429679\n",
-      "35.21390247344971 0.10739726688893424\n",
-      "48.478371143341064 -0.05339822582392689\n",
-      "40.725359201431274 0.06710397964579173\n",
-      "43.54443335533142 0.04774073080153538\n"
+      "65.0717043876648 0.12467616278770983\n",
+      "90.68930077552795 0.13929252527937125\n",
+      "76.80289697647095 0.12103943728777532\n",
+      "84.18350672721863 0.13730972361175564\n",
+      "59.469563484191895 0.10800808332018592\n",
+      "80.92852973937988 0.13700481460829453\n",
+      "85.58175778388977 0.1324956475675861\n",
+      "78.75041937828064 0.13073665118121205\n",
+      "80.78611373901367 0.12963646594644282\n",
+      "73.53622269630432 0.12723135400013533\n"
      ]
     },
     {
      "data": {
       "text/html": [
-       "
Model: \"LSTM\"\n",
+       "
Model: \"GRU\"\n",
        "
\n"
       ],
       "text/plain": [
-       "\u001b[1mModel: \"LSTM\"\u001b[0m\n"
+       "\u001b[1mModel: \"GRU\"\u001b[0m\n"
       ]
      },
      "metadata": {},
@@ -1746,11 +1371,11 @@
        "
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n",
        "┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃\n",
        "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n",
-       "│ lstm_10 (LSTM)                  │ (None, 60, 100)        │        48,000 │\n",
+       "│ gru_40 (GRU)                    │ (None, 45, 100)        │        36,300 │\n",
        "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
-       "│ lstm_11 (LSTM)                  │ (None, 100)            │        80,400 │\n",
+       "│ gru_41 (GRU)                    │ (None, 100)            │        60,600 │\n",
        "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
-       "│ dense_25 (Dense)                │ (None, 12)             │         1,212 │\n",
+       "│ dense_70 (Dense)                │ (None, 6)              │           606 │\n",
        "└─────────────────────────────────┴────────────────────────┴───────────────┘\n",
        "
\n"
       ],
@@ -1758,11 +1383,11 @@
        "┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n",
        "┃\u001b[1m \u001b[0m\u001b[1mLayer (type)                   \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mOutput Shape          \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m      Param #\u001b[0m\u001b[1m \u001b[0m┃\n",
        "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n",
-       "│ lstm_10 (\u001b[38;5;33mLSTM\u001b[0m)                  │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m60\u001b[0m, \u001b[38;5;34m100\u001b[0m)        │        \u001b[38;5;34m48,000\u001b[0m │\n",
+       "│ gru_40 (\u001b[38;5;33mGRU\u001b[0m)                    │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m45\u001b[0m, \u001b[38;5;34m100\u001b[0m)        │        \u001b[38;5;34m36,300\u001b[0m │\n",
        "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
-       "│ lstm_11 (\u001b[38;5;33mLSTM\u001b[0m)                  │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m100\u001b[0m)            │        \u001b[38;5;34m80,400\u001b[0m │\n",
+       "│ gru_41 (\u001b[38;5;33mGRU\u001b[0m)                    │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m100\u001b[0m)            │        \u001b[38;5;34m60,600\u001b[0m │\n",
        "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
-       "│ dense_25 (\u001b[38;5;33mDense\u001b[0m)                │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m12\u001b[0m)             │         \u001b[38;5;34m1,212\u001b[0m │\n",
+       "│ dense_70 (\u001b[38;5;33mDense\u001b[0m)                │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m6\u001b[0m)              │           \u001b[38;5;34m606\u001b[0m │\n",
        "└─────────────────────────────────┴────────────────────────┴───────────────┘\n"
       ]
      },
@@ -1772,11 +1397,11 @@
     {
      "data": {
       "text/html": [
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+       "
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-       "\u001b[1m Total params: \u001b[0m\u001b[38;5;34m129,612\u001b[0m (506.30 KB)\n"
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      "metadata": {},
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-      "28.626214027404785 -0.1006527298304624\n",
-      "35.30322313308716 -0.24341912522819487\n",
-      "27.737242698669434 -0.5102580838684908\n",
-      "32.53866934776306 -0.7130992597534002\n",
-      "34.27140927314758 -0.24619645358326023\n",
-      "R2 scores\n",
-      "Means:\n"
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-      "/usr/local/lib/python3.11/dist-packages/numpy/core/fromnumeric.py:3504: RuntimeWarning: Mean of empty slice.\n",
-      "  return _methods._mean(a, axis=axis, dtype=dtype,\n",
-      "/usr/local/lib/python3.11/dist-packages/numpy/core/_methods.py:129: RuntimeWarning: invalid value encountered in scalar divide\n",
-      "  ret = ret.dtype.type(ret / rcount)\n"
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TKANGRULSTM
150.0932910.078430-0.231738
120.0960050.051663-0.362725
9NaNNaNNaN
6NaNNaNNaN
3NaNNaNNaN
1NaNNaNNaN
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-       "        TKAN       GRU      LSTM\n",
-       "15  0.093291  0.078430 -0.231738\n",
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-       "9        NaN       NaN       NaN\n",
-       "6        NaN       NaN       NaN\n",
-       "3        NaN       NaN       NaN\n",
-       "1        NaN       NaN       NaN"
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-     "text": [
-      "/usr/local/lib/python3.11/dist-packages/numpy/core/fromnumeric.py:3504: RuntimeWarning: Mean of empty slice.\n",
-      "  return _methods._mean(a, axis=axis, dtype=dtype,\n",
-      "/usr/local/lib/python3.11/dist-packages/numpy/core/_methods.py:129: RuntimeWarning: invalid value encountered in scalar divide\n",
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TKANGRULSTM
150.0677050.0681940.078686
120.0673210.0688310.082159
9NaNNaNNaN
6NaNNaNNaN
3NaNNaNNaN
1NaNNaNNaN
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-       "        TKAN       GRU      LSTM\n",
-       "15  0.067705  0.068194  0.078686\n",
-       "12  0.067321  0.068831  0.082159\n",
-       "9        NaN       NaN       NaN\n",
-       "6        NaN       NaN       NaN\n",
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-       "1        NaN       NaN       NaN"
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-     "output_type": "stream",
-     "text": [
-      "/usr/local/lib/python3.11/dist-packages/numpy/core/_methods.py:206: RuntimeWarning: Degrees of freedom <= 0 for slice\n",
-      "  ret = _var(a, axis=axis, dtype=dtype, out=out, ddof=ddof,\n",
-      "/usr/local/lib/python3.11/dist-packages/numpy/core/_methods.py:163: RuntimeWarning: invalid value encountered in divide\n",
-      "  arrmean = um.true_divide(arrmean, div, out=arrmean,\n",
-      "/usr/local/lib/python3.11/dist-packages/numpy/core/_methods.py:198: RuntimeWarning: invalid value encountered in scalar divide\n",
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TKANGRULSTM
150.0055390.0159250.103254
120.0040600.0562630.219555
9NaNNaNNaN
6NaNNaNNaN
3NaNNaNNaN
1NaNNaNNaN
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-       "        TKAN       GRU      LSTM\n",
-       "15  0.005539  0.015925  0.103254\n",
-       "12  0.004060  0.056263  0.219555\n",
-       "9        NaN       NaN       NaN\n",
-       "6        NaN       NaN       NaN\n",
-       "3        NaN       NaN       NaN\n",
-       "1        NaN       NaN       NaN"
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-     "text": [
-      "/usr/local/lib/python3.11/dist-packages/numpy/core/_methods.py:206: RuntimeWarning: Degrees of freedom <= 0 for slice\n",
-      "  ret = _var(a, axis=axis, dtype=dtype, out=out, ddof=ddof,\n",
-      "/usr/local/lib/python3.11/dist-packages/numpy/core/_methods.py:163: RuntimeWarning: invalid value encountered in divide\n",
-      "  arrmean = um.true_divide(arrmean, div, out=arrmean,\n",
-      "/usr/local/lib/python3.11/dist-packages/numpy/core/_methods.py:198: RuntimeWarning: invalid value encountered in scalar divide\n",
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TKANGRULSTM
150.0002150.0005780.003284
120.0001480.0019780.006539
9NaNNaNNaN
6NaNNaNNaN
3NaNNaNNaN
1NaNNaNNaN
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-       "15  0.000215  0.000578  0.003284\n",
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-       "9        NaN       NaN       NaN\n",
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-      "/usr/local/lib/python3.11/dist-packages/numpy/core/fromnumeric.py:3504: RuntimeWarning: Mean of empty slice.\n",
-      "  return _methods._mean(a, axis=axis, dtype=dtype,\n",
-      "/usr/local/lib/python3.11/dist-packages/numpy/core/_methods.py:129: RuntimeWarning: invalid value encountered in scalar divide\n",
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15148.76306842.47829633.661120
12131.65323141.33398731.695352
9NaNNaNNaN
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3NaNNaNNaN
1NaNNaNNaN
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-      "  ret = _var(a, axis=axis, dtype=dtype, out=out, ddof=ddof,\n",
-      "/usr/local/lib/python3.11/dist-packages/numpy/core/_methods.py:163: RuntimeWarning: invalid value encountered in divide\n",
-      "  arrmean = um.true_divide(arrmean, div, out=arrmean,\n",
-      "/usr/local/lib/python3.11/dist-packages/numpy/core/_methods.py:198: RuntimeWarning: invalid value encountered in scalar divide\n",
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1514.6747052.7050711.335022
126.6137834.4866613.014970
9NaNNaNNaN
6NaNNaNNaN
3NaNNaNNaN
1NaNNaNNaN
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┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n",
-       "┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃\n",
-       "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n",
-       "│ tkan_20 (TKAN)                  │ (None, 45, 100)        │        41,750 │\n",
-       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
-       "│ tkan_21 (TKAN)                  │ (None, 100)            │        67,670 │\n",
-       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
-       "│ dense_30 (Dense)                │ (None, 9)              │           909 │\n",
-       "└─────────────────────────────────┴────────────────────────┴───────────────┘\n",
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-      ],
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-       "┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n",
-       "┃\u001b[1m \u001b[0m\u001b[1mLayer (type)                   \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mOutput Shape          \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m      Param #\u001b[0m\u001b[1m \u001b[0m┃\n",
-       "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n",
-       "│ tkan_20 (\u001b[38;5;33mTKAN\u001b[0m)                  │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m45\u001b[0m, \u001b[38;5;34m100\u001b[0m)        │        \u001b[38;5;34m41,750\u001b[0m │\n",
-       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
-       "│ tkan_21 (\u001b[38;5;33mTKAN\u001b[0m)                  │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m100\u001b[0m)            │        \u001b[38;5;34m67,670\u001b[0m │\n",
-       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
-       "│ dense_30 (\u001b[38;5;33mDense\u001b[0m)                │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m9\u001b[0m)              │           \u001b[38;5;34m909\u001b[0m │\n",
-       "└─────────────────────────────────┴────────────────────────┴───────────────┘\n"
-      ]
-     },
-     "metadata": {},
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-       "\u001b[1m Total params: \u001b[0m\u001b[38;5;34m110,329\u001b[0m (430.97 KB)\n"
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-       "\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m110,309\u001b[0m (430.89 KB)\n"
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-       "\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m20\u001b[0m (80.00 B)\n"
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-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "104.23471403121948 0.113891478849783\n",
-      "89.38614058494568 0.10592790984288752\n",
-      "106.43402981758118 0.10938055442300718\n",
-      "135.6341907978058 0.11293714586336041\n",
-      "125.27908515930176 0.11555530130229706\n"
-     ]
-    },
-    {
-     "data": {
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Model: \"GRU\"\n",
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-      ],
-      "text/plain": [
-       "\u001b[1mModel: \"GRU\"\u001b[0m\n"
-      ]
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┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n",
-       "┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃\n",
-       "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n",
-       "│ gru_20 (GRU)                    │ (None, 45, 100)        │        36,300 │\n",
-       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
-       "│ gru_21 (GRU)                    │ (None, 100)            │        60,600 │\n",
-       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
-       "│ dense_35 (Dense)                │ (None, 9)              │           909 │\n",
-       "└─────────────────────────────────┴────────────────────────┴───────────────┘\n",
-       "
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-      ],
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-       "┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n",
-       "┃\u001b[1m \u001b[0m\u001b[1mLayer (type)                   \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mOutput Shape          \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m      Param #\u001b[0m\u001b[1m \u001b[0m┃\n",
-       "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n",
-       "│ gru_20 (\u001b[38;5;33mGRU\u001b[0m)                    │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m45\u001b[0m, \u001b[38;5;34m100\u001b[0m)        │        \u001b[38;5;34m36,300\u001b[0m │\n",
-       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
-       "│ gru_21 (\u001b[38;5;33mGRU\u001b[0m)                    │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m100\u001b[0m)            │        \u001b[38;5;34m60,600\u001b[0m │\n",
-       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
-       "│ dense_35 (\u001b[38;5;33mDense\u001b[0m)                │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m9\u001b[0m)              │           \u001b[38;5;34m909\u001b[0m │\n",
-       "└─────────────────────────────────┴────────────────────────┴───────────────┘\n"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
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-      ],
-      "text/plain": [
-       "\u001b[1m Total params: \u001b[0m\u001b[38;5;34m97,809\u001b[0m (382.07 KB)\n"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
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-      ],
-      "text/plain": [
-       "\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m97,809\u001b[0m (382.07 KB)\n"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
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-       "
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-       "
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-      ],
-      "text/plain": [
-       "\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m0\u001b[0m (0.00 B)\n"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "29.72066307067871 0.1249179784635558\n",
-      "33.5255126953125 0.07318165013466399\n",
-      "38.62114071846008 -0.023410033110204745\n",
-      "35.293763875961304 0.0894183448972399\n",
-      "43.56008291244507 -0.023466014859413426\n"
-     ]
-    },
-    {
-     "data": {
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Model: \"LSTM\"\n",
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-      ],
-      "text/plain": [
-       "\u001b[1mModel: \"LSTM\"\u001b[0m\n"
-      ]
-     },
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-     "output_type": "display_data"
-    },
-    {
-     "data": {
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-       "
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n",
-       "┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃\n",
-       "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n",
-       "│ lstm_20 (LSTM)                  │ (None, 45, 100)        │        48,000 │\n",
-       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
-       "│ lstm_21 (LSTM)                  │ (None, 100)            │        80,400 │\n",
-       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
-       "│ dense_40 (Dense)                │ (None, 9)              │           909 │\n",
-       "└─────────────────────────────────┴────────────────────────┴───────────────┘\n",
-       "
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-      ],
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-       "┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n",
-       "┃\u001b[1m \u001b[0m\u001b[1mLayer (type)                   \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mOutput Shape          \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m      Param #\u001b[0m\u001b[1m \u001b[0m┃\n",
-       "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n",
-       "│ lstm_20 (\u001b[38;5;33mLSTM\u001b[0m)                  │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m45\u001b[0m, \u001b[38;5;34m100\u001b[0m)        │        \u001b[38;5;34m48,000\u001b[0m │\n",
-       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
-       "│ lstm_21 (\u001b[38;5;33mLSTM\u001b[0m)                  │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m100\u001b[0m)            │        \u001b[38;5;34m80,400\u001b[0m │\n",
-       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
-       "│ dense_40 (\u001b[38;5;33mDense\u001b[0m)                │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m9\u001b[0m)              │           \u001b[38;5;34m909\u001b[0m │\n",
-       "└─────────────────────────────────┴────────────────────────┴───────────────┘\n"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
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-       "\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m129,309\u001b[0m (505.11 KB)\n"
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-       "\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m0\u001b[0m (0.00 B)\n"
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-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "25.49171733856201 -0.3036703338256579\n",
-      "27.654677867889404 -0.14078671841214707\n",
-      "22.398143529891968 -0.0803994153254022\n",
-      "25.16021418571472 -0.06587595121316724\n",
-      "23.790441751480103 -0.025310879036213083\n",
-      "R2 scores\n",
-      "Means:\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/usr/local/lib/python3.11/dist-packages/numpy/core/fromnumeric.py:3504: RuntimeWarning: Mean of empty slice.\n",
-      "  return _methods._mean(a, axis=axis, dtype=dtype,\n",
-      "/usr/local/lib/python3.11/dist-packages/numpy/core/_methods.py:129: RuntimeWarning: invalid value encountered in scalar divide\n",
-      "  ret = ret.dtype.type(ret / rcount)\n"
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TKANGRULSTM
150.0932910.078430-0.231738
120.0960050.051663-0.362725
90.1115380.048128-0.123209
6NaNNaNNaN
3NaNNaNNaN
1NaNNaNNaN
\n",
-       "
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-      ],
-      "text/plain": [
-       "        TKAN       GRU      LSTM\n",
-       "15  0.093291  0.078430 -0.231738\n",
-       "12  0.096005  0.051663 -0.362725\n",
-       "9   0.111538  0.048128 -0.123209\n",
-       "6        NaN       NaN       NaN\n",
-       "3        NaN       NaN       NaN\n",
-       "1        NaN       NaN       NaN"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/usr/local/lib/python3.11/dist-packages/numpy/core/fromnumeric.py:3504: RuntimeWarning: Mean of empty slice.\n",
-      "  return _methods._mean(a, axis=axis, dtype=dtype,\n",
-      "/usr/local/lib/python3.11/dist-packages/numpy/core/_methods.py:129: RuntimeWarning: invalid value encountered in scalar divide\n",
-      "  ret = ret.dtype.type(ret / rcount)\n"
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TKANGRULSTM
150.0677050.0681940.078686
120.0673210.0688310.082159
90.0667170.0688820.074797
6NaNNaNNaN
3NaNNaNNaN
1NaNNaNNaN
\n",
-       "
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-      ],
-      "text/plain": [
-       "        TKAN       GRU      LSTM\n",
-       "15  0.067705  0.068194  0.078686\n",
-       "12  0.067321  0.068831  0.082159\n",
-       "9   0.066717  0.068882  0.074797\n",
-       "6        NaN       NaN       NaN\n",
-       "3        NaN       NaN       NaN\n",
-       "1        NaN       NaN       NaN"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
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-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Std:\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/usr/local/lib/python3.11/dist-packages/numpy/core/_methods.py:206: RuntimeWarning: Degrees of freedom <= 0 for slice\n",
-      "  ret = _var(a, axis=axis, dtype=dtype, out=out, ddof=ddof,\n",
-      "/usr/local/lib/python3.11/dist-packages/numpy/core/_methods.py:163: RuntimeWarning: invalid value encountered in divide\n",
-      "  arrmean = um.true_divide(arrmean, div, out=arrmean,\n",
-      "/usr/local/lib/python3.11/dist-packages/numpy/core/_methods.py:198: RuntimeWarning: invalid value encountered in scalar divide\n",
-      "  ret = ret.dtype.type(ret / rcount)\n"
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TKANGRULSTM
150.0055390.0159250.103254
120.0040600.0562630.219555
90.0034570.0607830.097549
6NaNNaNNaN
3NaNNaNNaN
1NaNNaNNaN
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-       "        TKAN       GRU      LSTM\n",
-       "15  0.005539  0.015925  0.103254\n",
-       "12  0.004060  0.056263  0.219555\n",
-       "9   0.003457  0.060783  0.097549\n",
-       "6        NaN       NaN       NaN\n",
-       "3        NaN       NaN       NaN\n",
-       "1        NaN       NaN       NaN"
-      ]
-     },
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-     "output_type": "display_data"
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/usr/local/lib/python3.11/dist-packages/numpy/core/_methods.py:206: RuntimeWarning: Degrees of freedom <= 0 for slice\n",
-      "  ret = _var(a, axis=axis, dtype=dtype, out=out, ddof=ddof,\n",
-      "/usr/local/lib/python3.11/dist-packages/numpy/core/_methods.py:163: RuntimeWarning: invalid value encountered in divide\n",
-      "  arrmean = um.true_divide(arrmean, div, out=arrmean,\n",
-      "/usr/local/lib/python3.11/dist-packages/numpy/core/_methods.py:198: RuntimeWarning: invalid value encountered in scalar divide\n",
-      "  ret = ret.dtype.type(ret / rcount)\n"
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TKANGRULSTM
150.0002150.0005780.003284
120.0001480.0019780.006539
90.0001310.0021420.003162
6NaNNaNNaN
3NaNNaNNaN
1NaNNaNNaN
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-       "        TKAN       GRU      LSTM\n",
-       "15  0.000215  0.000578  0.003284\n",
-       "12  0.000148  0.001978  0.006539\n",
-       "9   0.000131  0.002142  0.003162\n",
-       "6        NaN       NaN       NaN\n",
-       "3        NaN       NaN       NaN\n",
-       "1        NaN       NaN       NaN"
-      ]
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-     "output_type": "stream",
-     "text": [
-      "Training Times\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/usr/local/lib/python3.11/dist-packages/numpy/core/fromnumeric.py:3504: RuntimeWarning: Mean of empty slice.\n",
-      "  return _methods._mean(a, axis=axis, dtype=dtype,\n",
-      "/usr/local/lib/python3.11/dist-packages/numpy/core/_methods.py:129: RuntimeWarning: invalid value encountered in scalar divide\n",
-      "  ret = ret.dtype.type(ret / rcount)\n"
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TKANGRULSTM
15148.76306842.47829633.661120
12131.65323141.33398731.695352
9112.19363236.14423324.899039
6NaNNaNNaN
3NaNNaNNaN
1NaNNaNNaN
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-       "
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-      "text/plain": [
-       "          TKAN        GRU       LSTM\n",
-       "15  148.763068  42.478296  33.661120\n",
-       "12  131.653231  41.333987  31.695352\n",
-       "9   112.193632  36.144233  24.899039\n",
-       "6          NaN        NaN        NaN\n",
-       "3          NaN        NaN        NaN\n",
-       "1          NaN        NaN        NaN"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/usr/local/lib/python3.11/dist-packages/numpy/core/_methods.py:206: RuntimeWarning: Degrees of freedom <= 0 for slice\n",
-      "  ret = _var(a, axis=axis, dtype=dtype, out=out, ddof=ddof,\n",
-      "/usr/local/lib/python3.11/dist-packages/numpy/core/_methods.py:163: RuntimeWarning: invalid value encountered in divide\n",
-      "  arrmean = um.true_divide(arrmean, div, out=arrmean,\n",
-      "/usr/local/lib/python3.11/dist-packages/numpy/core/_methods.py:198: RuntimeWarning: invalid value encountered in scalar divide\n",
-      "  ret = ret.dtype.type(ret / rcount)\n"
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TKANGRULSTM
1514.6747052.7050711.335022
126.6137834.4866613.014970
916.3547424.6898431.760482
6NaNNaNNaN
3NaNNaNNaN
1NaNNaNNaN
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-       "         TKAN       GRU      LSTM\n",
-       "15  14.674705  2.705071  1.335022\n",
-       "12   6.613783  4.486661  3.014970\n",
-       "9   16.354742  4.689843  1.760482\n",
-       "6         NaN       NaN       NaN\n",
-       "3         NaN       NaN       NaN\n",
-       "1         NaN       NaN       NaN"
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┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n",
-       "┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃\n",
-       "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n",
-       "│ tkan_30 (TKAN)                  │ (None, 45, 100)        │        41,750 │\n",
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-       "│ tkan_31 (TKAN)                  │ (None, 100)            │        67,670 │\n",
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-       "│ dense_45 (Dense)                │ (None, 6)              │           606 │\n",
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-       "┃\u001b[1m \u001b[0m\u001b[1mLayer (type)                   \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mOutput Shape          \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m      Param #\u001b[0m\u001b[1m \u001b[0m┃\n",
-       "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n",
-       "│ tkan_30 (\u001b[38;5;33mTKAN\u001b[0m)                  │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m45\u001b[0m, \u001b[38;5;34m100\u001b[0m)        │        \u001b[38;5;34m41,750\u001b[0m │\n",
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-       "│ tkan_31 (\u001b[38;5;33mTKAN\u001b[0m)                  │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m100\u001b[0m)            │        \u001b[38;5;34m67,670\u001b[0m │\n",
-       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
-       "│ dense_45 (\u001b[38;5;33mDense\u001b[0m)                │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m6\u001b[0m)              │           \u001b[38;5;34m606\u001b[0m │\n",
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-     "text": [
-      "77.3258445262909 0.1280814725052942\n",
-      "114.44928884506226 0.14017116370653385\n",
-      "104.74958324432373 0.126116634620875\n",
-      "101.73448276519775 0.13543281159237028\n",
-      "88.49818539619446 0.13133054407465156\n"
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┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n",
-       "┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃\n",
-       "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n",
-       "│ gru_30 (GRU)                    │ (None, 45, 100)        │        36,300 │\n",
-       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
-       "│ gru_31 (GRU)                    │ (None, 100)            │        60,600 │\n",
-       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
-       "│ dense_50 (Dense)                │ (None, 6)              │           606 │\n",
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-       "┃\u001b[1m \u001b[0m\u001b[1mLayer (type)                   \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mOutput Shape          \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m      Param #\u001b[0m\u001b[1m \u001b[0m┃\n",
-       "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n",
-       "│ gru_30 (\u001b[38;5;33mGRU\u001b[0m)                    │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m45\u001b[0m, \u001b[38;5;34m100\u001b[0m)        │        \u001b[38;5;34m36,300\u001b[0m │\n",
-       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
-       "│ gru_31 (\u001b[38;5;33mGRU\u001b[0m)                    │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m100\u001b[0m)            │        \u001b[38;5;34m60,600\u001b[0m │\n",
-       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
-       "│ dense_50 (\u001b[38;5;33mDense\u001b[0m)                │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m6\u001b[0m)              │           \u001b[38;5;34m606\u001b[0m │\n",
-       "└─────────────────────────────────┴────────────────────────┴───────────────┘\n"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/html": [
-       "
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-      ],
-      "text/plain": [
-       "\u001b[1m Total params: \u001b[0m\u001b[38;5;34m97,506\u001b[0m (380.88 KB)\n"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/html": [
-       "
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-       "
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-      ],
-      "text/plain": [
-       "\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m97,506\u001b[0m (380.88 KB)\n"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/html": [
-       "
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-       "
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-      ],
-      "text/plain": [
-       "\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m0\u001b[0m (0.00 B)\n"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "40.4565007686615 0.13588272560079817\n",
-      "32.21065616607666 0.150907506214383\n",
-      "41.915128231048584 0.09900284704797373\n",
-      "40.109740018844604 0.06818254449404637\n",
-      "40.55113649368286 0.14480364148103195\n"
+      "26.26027750968933 0.1602740337153421\n",
+      "30.87123155593872 0.12435047691678952\n",
+      "30.425692319869995 0.10110813722797496\n",
+      "29.073087215423584 0.13987663037711742\n",
+      "27.583446502685547 0.12691294524178082\n",
+      "30.0588481426239 0.09291668323995494\n",
+      "30.896180629730225 0.13374339087226397\n",
+      "26.069262266159058 0.13703060900398636\n",
+      "26.07837224006653 0.14521618469459105\n",
+      "28.18198537826538 0.13697203267595567\n"
      ]
     },
     {
@@ -3422,11 +1468,11 @@
        "
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n",
        "┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃\n",
        "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n",
-       "│ lstm_30 (LSTM)                  │ (None, 45, 100)        │        48,000 │\n",
+       "│ lstm_40 (LSTM)                  │ (None, 45, 100)        │        48,000 │\n",
        "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
-       "│ lstm_31 (LSTM)                  │ (None, 100)            │        80,400 │\n",
+       "│ lstm_41 (LSTM)                  │ (None, 100)            │        80,400 │\n",
        "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
-       "│ dense_55 (Dense)                │ (None, 6)              │           606 │\n",
+       "│ dense_80 (Dense)                │ (None, 6)              │           606 │\n",
        "└─────────────────────────────────┴────────────────────────┴───────────────┘\n",
        "
\n"
       ],
@@ -3434,435 +1480,51 @@
        "┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n",
        "┃\u001b[1m \u001b[0m\u001b[1mLayer (type)                   \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mOutput Shape          \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m      Param #\u001b[0m\u001b[1m \u001b[0m┃\n",
        "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n",
-       "│ lstm_30 (\u001b[38;5;33mLSTM\u001b[0m)                  │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m45\u001b[0m, \u001b[38;5;34m100\u001b[0m)        │        \u001b[38;5;34m48,000\u001b[0m │\n",
+       "│ lstm_40 (\u001b[38;5;33mLSTM\u001b[0m)                  │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m45\u001b[0m, \u001b[38;5;34m100\u001b[0m)        │        \u001b[38;5;34m48,000\u001b[0m │\n",
        "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
-       "│ lstm_31 (\u001b[38;5;33mLSTM\u001b[0m)                  │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m100\u001b[0m)            │        \u001b[38;5;34m80,400\u001b[0m │\n",
+       "│ lstm_41 (\u001b[38;5;33mLSTM\u001b[0m)                  │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m100\u001b[0m)            │        \u001b[38;5;34m80,400\u001b[0m │\n",
        "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
-       "│ dense_55 (\u001b[38;5;33mDense\u001b[0m)                │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m6\u001b[0m)              │           \u001b[38;5;34m606\u001b[0m │\n",
+       "│ dense_80 (\u001b[38;5;33mDense\u001b[0m)                │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m6\u001b[0m)              │           \u001b[38;5;34m606\u001b[0m │\n",
        "└─────────────────────────────────┴────────────────────────┴───────────────┘\n"
       ]
      },
      "metadata": {},
-     "output_type": "display_data"
-    },
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-     "data": {
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-       "
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-      ],
-      "text/plain": [
-       "\u001b[1m Total params: \u001b[0m\u001b[38;5;34m129,006\u001b[0m (503.93 KB)\n"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
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-      ],
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-       "\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m129,006\u001b[0m (503.93 KB)\n"
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-       "\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m0\u001b[0m (0.00 B)\n"
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-     "metadata": {},
-     "output_type": "display_data"
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-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "23.99752426147461 0.03173612065905046\n",
-      "25.493926286697388 -0.05948194958746694\n",
-      "33.9563672542572 0.12418423914604533\n",
-      "25.076443910598755 -0.09484002694483258\n",
-      "23.51260232925415 -0.021994532852705695\n",
-      "R2 scores\n",
-      "Means:\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/usr/local/lib/python3.11/dist-packages/numpy/core/fromnumeric.py:3504: RuntimeWarning: Mean of empty slice.\n",
-      "  return _methods._mean(a, axis=axis, dtype=dtype,\n",
-      "/usr/local/lib/python3.11/dist-packages/numpy/core/_methods.py:129: RuntimeWarning: invalid value encountered in scalar divide\n",
-      "  ret = ret.dtype.type(ret / rcount)\n"
-     ]
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TKANGRULSTM
150.0932910.078430-0.231738
120.0960050.051663-0.362725
90.1115380.048128-0.123209
60.1322270.119756-0.004079
3NaNNaNNaN
1NaNNaNNaN
\n",
-       "
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-      ],
-      "text/plain": [
-       "        TKAN       GRU      LSTM\n",
-       "15  0.093291  0.078430 -0.231738\n",
-       "12  0.096005  0.051663 -0.362725\n",
-       "9   0.111538  0.048128 -0.123209\n",
-       "6   0.132227  0.119756 -0.004079\n",
-       "3        NaN       NaN       NaN\n",
-       "1        NaN       NaN       NaN"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/usr/local/lib/python3.11/dist-packages/numpy/core/fromnumeric.py:3504: RuntimeWarning: Mean of empty slice.\n",
-      "  return _methods._mean(a, axis=axis, dtype=dtype,\n",
-      "/usr/local/lib/python3.11/dist-packages/numpy/core/_methods.py:129: RuntimeWarning: invalid value encountered in scalar divide\n",
-      "  ret = ret.dtype.type(ret / rcount)\n"
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TKANGRULSTM
150.0677050.0681940.078686
120.0673210.0688310.082159
90.0667170.0688820.074797
60.0661310.0664700.070901
3NaNNaNNaN
1NaNNaNNaN
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-       "
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-      ],
-      "text/plain": [
-       "        TKAN       GRU      LSTM\n",
-       "15  0.067705  0.068194  0.078686\n",
-       "12  0.067321  0.068831  0.082159\n",
-       "9   0.066717  0.068882  0.074797\n",
-       "6   0.066131  0.066470  0.070901\n",
-       "3        NaN       NaN       NaN\n",
-       "1        NaN       NaN       NaN"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Std:\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/usr/local/lib/python3.11/dist-packages/numpy/core/_methods.py:206: RuntimeWarning: Degrees of freedom <= 0 for slice\n",
-      "  ret = _var(a, axis=axis, dtype=dtype, out=out, ddof=ddof,\n",
-      "/usr/local/lib/python3.11/dist-packages/numpy/core/_methods.py:163: RuntimeWarning: invalid value encountered in divide\n",
-      "  arrmean = um.true_divide(arrmean, div, out=arrmean,\n",
-      "/usr/local/lib/python3.11/dist-packages/numpy/core/_methods.py:198: RuntimeWarning: invalid value encountered in scalar divide\n",
-      "  ret = ret.dtype.type(ret / rcount)\n"
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TKANGRULSTM
150.0055390.0159250.103254
120.0040600.0562630.219555
90.0034570.0607830.097549
60.0050740.0314590.076632
3NaNNaNNaN
1NaNNaNNaN
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-       "
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+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "
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+       "
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       ],
       "text/plain": [
-       "        TKAN       GRU      LSTM\n",
-       "15  0.005539  0.015925  0.103254\n",
-       "12  0.004060  0.056263  0.219555\n",
-       "9   0.003457  0.060783  0.097549\n",
-       "6   0.005074  0.031459  0.076632\n",
-       "3        NaN       NaN       NaN\n",
-       "1        NaN       NaN       NaN"
+       "\u001b[1m Total params: \u001b[0m\u001b[38;5;34m129,006\u001b[0m (503.93 KB)\n"
       ]
      },
      "metadata": {},
      "output_type": "display_data"
     },
     {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/usr/local/lib/python3.11/dist-packages/numpy/core/_methods.py:206: RuntimeWarning: Degrees of freedom <= 0 for slice\n",
-      "  ret = _var(a, axis=axis, dtype=dtype, out=out, ddof=ddof,\n",
-      "/usr/local/lib/python3.11/dist-packages/numpy/core/_methods.py:163: RuntimeWarning: invalid value encountered in divide\n",
-      "  arrmean = um.true_divide(arrmean, div, out=arrmean,\n",
-      "/usr/local/lib/python3.11/dist-packages/numpy/core/_methods.py:198: RuntimeWarning: invalid value encountered in scalar divide\n",
-      "  ret = ret.dtype.type(ret / rcount)\n"
-     ]
+     "data": {
+      "text/html": [
+       "
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+       "
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+      ],
+      "text/plain": [
+       "\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m129,006\u001b[0m (503.93 KB)\n"
+      ]
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TKANGRULSTM
150.0002150.0005780.003284
120.0001480.0019780.006539
90.0001310.0021420.003162
60.0001990.0011510.002695
3NaNNaNNaN
1NaNNaNNaN
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+       "
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+       "
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       ],
       "text/plain": [
-       "        TKAN       GRU      LSTM\n",
-       "15  0.000215  0.000578  0.003284\n",
-       "12  0.000148  0.001978  0.006539\n",
-       "9   0.000131  0.002142  0.003162\n",
-       "6   0.000199  0.001151  0.002695\n",
-       "3        NaN       NaN       NaN\n",
-       "1        NaN       NaN       NaN"
+       "\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m0\u001b[0m (0.00 B)\n"
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      },
      "metadata": {},
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-      "Training Times\n"
+      "25.56489086151123 0.07762226752057655\n",
+      "17.17194938659668 -0.030710740566574007\n",
+      "22.08793616294861 0.12124885788661292\n",
+      "18.8874089717865 -0.19992596769356452\n",
+      "18.460254669189453 -0.03568727060662086\n",
+      "19.47718334197998 -0.1732379052017987\n",
+      "18.813477754592896 -0.0515297041407121\n",
+      "18.96723985671997 -0.00603457861723435\n",
+      "17.978565216064453 0.06545331792210407\n",
+      "18.76132583618164 -0.21833925712011495\n"
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-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/usr/local/lib/python3.11/dist-packages/numpy/core/fromnumeric.py:3504: RuntimeWarning: Mean of empty slice.\n",
-      "  return _methods._mean(a, axis=axis, dtype=dtype,\n",
-      "/usr/local/lib/python3.11/dist-packages/numpy/core/_methods.py:129: RuntimeWarning: invalid value encountered in scalar divide\n",
-      "  ret = ret.dtype.type(ret / rcount)\n"
-     ]
+     "data": {
+      "text/html": [
+       "
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+       "
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+      ],
+      "text/plain": [
+       "\u001b[1mModel: \"TKAN\"\u001b[0m\n"
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TKANGRULSTM
15148.76306842.47829633.661120
12131.65323141.33398731.695352
9112.19363236.14423324.899039
697.35147739.04863226.407373
3NaNNaNNaN
1NaNNaNNaN
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-       "
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+       "
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n",
+       "┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃\n",
+       "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n",
+       "│ tkan_60 (TKAN)                  │ (None, 45, 100)        │        41,316 │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ tkan_61 (TKAN)                  │ (None, 100)            │        67,670 │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ dense_90 (Dense)                │ (None, 9)              │           909 │\n",
+       "└─────────────────────────────────┴────────────────────────┴───────────────┘\n",
+       "
\n"
       ],
       "text/plain": [
-       "          TKAN        GRU       LSTM\n",
-       "15  148.763068  42.478296  33.661120\n",
-       "12  131.653231  41.333987  31.695352\n",
-       "9   112.193632  36.144233  24.899039\n",
-       "6    97.351477  39.048632  26.407373\n",
-       "3          NaN        NaN        NaN\n",
-       "1          NaN        NaN        NaN"
+       "┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n",
+       "┃\u001b[1m \u001b[0m\u001b[1mLayer (type)                   \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mOutput Shape          \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m      Param #\u001b[0m\u001b[1m \u001b[0m┃\n",
+       "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n",
+       "│ tkan_60 (\u001b[38;5;33mTKAN\u001b[0m)                  │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m45\u001b[0m, \u001b[38;5;34m100\u001b[0m)        │        \u001b[38;5;34m41,316\u001b[0m │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ tkan_61 (\u001b[38;5;33mTKAN\u001b[0m)                  │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m100\u001b[0m)            │        \u001b[38;5;34m67,670\u001b[0m │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ dense_90 (\u001b[38;5;33mDense\u001b[0m)                │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m9\u001b[0m)              │           \u001b[38;5;34m909\u001b[0m │\n",
+       "└─────────────────────────────────┴────────────────────────┴───────────────┘\n"
       ]
      },
      "metadata": {},
      "output_type": "display_data"
     },
     {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/usr/local/lib/python3.11/dist-packages/numpy/core/_methods.py:206: RuntimeWarning: Degrees of freedom <= 0 for slice\n",
-      "  ret = _var(a, axis=axis, dtype=dtype, out=out, ddof=ddof,\n",
-      "/usr/local/lib/python3.11/dist-packages/numpy/core/_methods.py:163: RuntimeWarning: invalid value encountered in divide\n",
-      "  arrmean = um.true_divide(arrmean, div, out=arrmean,\n",
-      "/usr/local/lib/python3.11/dist-packages/numpy/core/_methods.py:198: RuntimeWarning: invalid value encountered in scalar divide\n",
-      "  ret = ret.dtype.type(ret / rcount)\n"
-     ]
+     "data": {
+      "text/html": [
+       "
 Total params: 109,895 (429.28 KB)\n",
+       "
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+      ],
+      "text/plain": [
+       "\u001b[1m Total params: \u001b[0m\u001b[38;5;34m109,895\u001b[0m (429.28 KB)\n"
+      ]
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+     "output_type": "display_data"
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TKANGRULSTM
1514.6747052.7050711.335022
126.6137834.4866613.014970
916.3547424.6898431.760482
613.0052653.4739113.841358
3NaNNaNNaN
1NaNNaNNaN
\n",
-       "
"
+       "
 Trainable params: 109,875 (429.20 KB)\n",
+       "
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       ],
       "text/plain": [
-       "         TKAN       GRU      LSTM\n",
-       "15  14.674705  2.705071  1.335022\n",
-       "12   6.613783  4.486661  3.014970\n",
-       "9   16.354742  4.689843  1.760482\n",
-       "6   13.005265  3.473911  3.841358\n",
-       "3         NaN       NaN       NaN\n",
-       "1         NaN       NaN       NaN"
+       "\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m109,875\u001b[0m (429.20 KB)\n"
       ]
      },
      "metadata": {},
@@ -4060,11 +1617,40 @@
     {
      "data": {
       "text/html": [
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 Non-trainable params: 20 (80.00 B)\n",
        "
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       "text/plain": [
-       "\u001b[1mModel: \"TKAN\"\u001b[0m\n"
+       "\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m20\u001b[0m (80.00 B)\n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "79.56117129325867 0.10413605110577702\n",
+      "95.19019293785095 0.10785571960353318\n",
+      "62.058950901031494 0.1022740221679086\n",
+      "57.313427448272705 0.09659038594513981\n",
+      "95.39828181266785 0.11798299575914914\n",
+      "109.50233817100525 0.11471786200051105\n",
+      "91.66243028640747 0.10141240448038549\n",
+      "83.4484133720398 0.11271609311366479\n",
+      "80.45564818382263 0.10729036960326731\n",
+      "89.72633719444275 0.10601871726343631\n"
+     ]
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+    {
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+      "text/html": [
+       "
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       ]
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      "metadata": {},
@@ -4076,11 +1662,11 @@
        "
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n",
        "┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃\n",
        "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n",
-       "│ tkan_40 (TKAN)                  │ (None, 45, 100)        │        41,750 │\n",
+       "│ gru_60 (GRU)                    │ (None, 45, 100)        │        36,300 │\n",
        "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
-       "│ tkan_41 (TKAN)                  │ (None, 100)            │        67,670 │\n",
+       "│ gru_61 (GRU)                    │ (None, 100)            │        60,600 │\n",
        "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
-       "│ dense_60 (Dense)                │ (None, 3)              │           303 │\n",
+       "│ dense_100 (Dense)               │ (None, 9)              │           909 │\n",
        "└─────────────────────────────────┴────────────────────────┴───────────────┘\n",
        "
\n"
       ],
@@ -4088,11 +1674,11 @@
        "┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n",
        "┃\u001b[1m \u001b[0m\u001b[1mLayer (type)                   \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mOutput Shape          \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m      Param #\u001b[0m\u001b[1m \u001b[0m┃\n",
        "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n",
-       "│ tkan_40 (\u001b[38;5;33mTKAN\u001b[0m)                  │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m45\u001b[0m, \u001b[38;5;34m100\u001b[0m)        │        \u001b[38;5;34m41,750\u001b[0m │\n",
+       "│ gru_60 (\u001b[38;5;33mGRU\u001b[0m)                    │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m45\u001b[0m, \u001b[38;5;34m100\u001b[0m)        │        \u001b[38;5;34m36,300\u001b[0m │\n",
        "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
-       "│ tkan_41 (\u001b[38;5;33mTKAN\u001b[0m)                  │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m100\u001b[0m)            │        \u001b[38;5;34m67,670\u001b[0m │\n",
+       "│ gru_61 (\u001b[38;5;33mGRU\u001b[0m)                    │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m100\u001b[0m)            │        \u001b[38;5;34m60,600\u001b[0m │\n",
        "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
-       "│ dense_60 (\u001b[38;5;33mDense\u001b[0m)                │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m3\u001b[0m)              │           \u001b[38;5;34m303\u001b[0m │\n",
+       "│ dense_100 (\u001b[38;5;33mDense\u001b[0m)               │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m9\u001b[0m)              │           \u001b[38;5;34m909\u001b[0m │\n",
        "└─────────────────────────────────┴────────────────────────┴───────────────┘\n"
       ]
      },
@@ -4102,11 +1688,11 @@
     {
      "data": {
       "text/html": [
-       "
 Total params: 109,723 (428.61 KB)\n",
+       "
 Total params: 97,809 (382.07 KB)\n",
        "
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       ],
       "text/plain": [
-       "\u001b[1m Total params: \u001b[0m\u001b[38;5;34m109,723\u001b[0m (428.61 KB)\n"
+       "\u001b[1m Total params: \u001b[0m\u001b[38;5;34m97,809\u001b[0m (382.07 KB)\n"
       ]
      },
      "metadata": {},
@@ -4115,11 +1701,11 @@
     {
      "data": {
       "text/html": [
-       "
 Trainable params: 109,703 (428.53 KB)\n",
+       "
 Trainable params: 97,809 (382.07 KB)\n",
        "
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       ],
       "text/plain": [
-       "\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m109,703\u001b[0m (428.53 KB)\n"
+       "\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m97,809\u001b[0m (382.07 KB)\n"
       ]
      },
      "metadata": {},
@@ -4128,11 +1714,11 @@
     {
      "data": {
       "text/html": [
-       "
 Non-trainable params: 20 (80.00 B)\n",
+       "
 Non-trainable params: 0 (0.00 B)\n",
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       "text/plain": [
-       "\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m20\u001b[0m (80.00 B)\n"
+       "\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m0\u001b[0m (0.00 B)\n"
       ]
      },
      "metadata": {},
@@ -4142,21 +1728,26 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "92.45071959495544 0.1916116569043532\n",
-      "94.640451669693 0.19454637958748155\n",
-      "73.71659445762634 0.19117099482549302\n",
-      "95.40752124786377 0.19110640326041053\n",
-      "85.31889510154724 0.19614372723219617\n"
+      "22.387739181518555 0.12872665220206386\n",
+      "30.9358811378479 0.07007529147404491\n",
+      "30.65935730934143 0.06094348103987125\n",
+      "22.681428909301758 0.11531462257235203\n",
+      "30.2576744556427 -0.009184511914249627\n",
+      "23.401977062225342 0.1145808233763385\n",
+      "29.867743015289307 -0.0020916084892752293\n",
+      "21.44379758834839 0.13675731715603687\n",
+      "32.31744432449341 0.08318358609980472\n",
+      "36.52803301811218 -0.02434265870166709\n"
      ]
     },
     {
      "data": {
       "text/html": [
-       "
Model: \"GRU\"\n",
+       "
Model: \"LSTM\"\n",
        "
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       ],
       "text/plain": [
-       "\u001b[1mModel: \"GRU\"\u001b[0m\n"
+       "\u001b[1mModel: \"LSTM\"\u001b[0m\n"
       ]
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      "metadata": {},
@@ -4168,11 +1759,11 @@
        "
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n",
        "┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃\n",
        "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n",
-       "│ gru_40 (GRU)                    │ (None, 45, 100)        │        36,300 │\n",
+       "│ lstm_60 (LSTM)                  │ (None, 45, 100)        │        48,000 │\n",
        "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
-       "│ gru_41 (GRU)                    │ (None, 100)            │        60,600 │\n",
+       "│ lstm_61 (LSTM)                  │ (None, 100)            │        80,400 │\n",
        "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
-       "│ dense_65 (Dense)                │ (None, 3)              │           303 │\n",
+       "│ dense_110 (Dense)               │ (None, 9)              │           909 │\n",
        "└─────────────────────────────────┴────────────────────────┴───────────────┘\n",
        "
\n"
       ],
@@ -4180,11 +1771,11 @@
        "┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n",
        "┃\u001b[1m \u001b[0m\u001b[1mLayer (type)                   \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mOutput Shape          \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m      Param #\u001b[0m\u001b[1m \u001b[0m┃\n",
        "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n",
-       "│ gru_40 (\u001b[38;5;33mGRU\u001b[0m)                    │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m45\u001b[0m, \u001b[38;5;34m100\u001b[0m)        │        \u001b[38;5;34m36,300\u001b[0m │\n",
+       "│ lstm_60 (\u001b[38;5;33mLSTM\u001b[0m)                  │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m45\u001b[0m, \u001b[38;5;34m100\u001b[0m)        │        \u001b[38;5;34m48,000\u001b[0m │\n",
        "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
-       "│ gru_41 (\u001b[38;5;33mGRU\u001b[0m)                    │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m100\u001b[0m)            │        \u001b[38;5;34m60,600\u001b[0m │\n",
+       "│ lstm_61 (\u001b[38;5;33mLSTM\u001b[0m)                  │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m100\u001b[0m)            │        \u001b[38;5;34m80,400\u001b[0m │\n",
        "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
-       "│ dense_65 (\u001b[38;5;33mDense\u001b[0m)                │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m3\u001b[0m)              │           \u001b[38;5;34m303\u001b[0m │\n",
+       "│ dense_110 (\u001b[38;5;33mDense\u001b[0m)               │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m9\u001b[0m)              │           \u001b[38;5;34m909\u001b[0m │\n",
        "└─────────────────────────────────┴────────────────────────┴───────────────┘\n"
       ]
      },
@@ -4194,11 +1785,11 @@
     {
      "data": {
       "text/html": [
-       "
 Total params: 97,203 (379.70 KB)\n",
+       "
 Total params: 129,309 (505.11 KB)\n",
        "
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       ],
       "text/plain": [
-       "\u001b[1m Total params: \u001b[0m\u001b[38;5;34m97,203\u001b[0m (379.70 KB)\n"
+       "\u001b[1m Total params: \u001b[0m\u001b[38;5;34m129,309\u001b[0m (505.11 KB)\n"
       ]
      },
      "metadata": {},
@@ -4207,11 +1798,11 @@
     {
      "data": {
       "text/html": [
-       "
 Trainable params: 97,203 (379.70 KB)\n",
+       "
 Trainable params: 129,309 (505.11 KB)\n",
        "
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       ],
       "text/plain": [
-       "\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m97,203\u001b[0m (379.70 KB)\n"
+       "\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m129,309\u001b[0m (505.11 KB)\n"
       ]
      },
      "metadata": {},
@@ -4234,21 +1825,26 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "34.47401285171509 0.23517545876629728\n",
-      "32.61530804634094 0.23820733972132835\n",
-      "38.93267202377319 0.23710632242677557\n",
-      "32.61954689025879 0.23525012980273963\n",
-      "33.25591826438904 0.24304388963816356\n"
+      "20.542985439300537 -0.058979852588216475\n",
+      "19.61976170539856 -0.07973171479104875\n",
+      "19.607908487319946 -0.15736860736964103\n",
+      "17.53559136390686 -0.09773126198658748\n",
+      "19.434728860855103 -0.39964713298492527\n",
+      "20.465200901031494 -0.2428380152707189\n",
+      "18.985889673233032 -0.5012525957302113\n",
+      "20.644855260849 -0.2738693890416318\n",
+      "19.245380401611328 -0.05146921829097126\n",
+      "19.828939199447632 -0.0008553086817381464\n"
      ]
     },
     {
      "data": {
       "text/html": [
-       "
Model: \"LSTM\"\n",
+       "
Model: \"TKAN\"\n",
        "
\n"
       ],
       "text/plain": [
-       "\u001b[1mModel: \"LSTM\"\u001b[0m\n"
+       "\u001b[1mModel: \"TKAN\"\u001b[0m\n"
       ]
      },
      "metadata": {},
@@ -4260,11 +1856,11 @@
        "
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n",
        "┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃\n",
        "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n",
-       "│ lstm_40 (LSTM)                  │ (None, 45, 100)        │        48,000 │\n",
+       "│ tkan_80 (TKAN)                  │ (None, 60, 100)        │        41,316 │\n",
        "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
-       "│ lstm_41 (LSTM)                  │ (None, 100)            │        80,400 │\n",
+       "│ tkan_81 (TKAN)                  │ (None, 100)            │        67,670 │\n",
        "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
-       "│ dense_70 (Dense)                │ (None, 3)              │           303 │\n",
+       "│ dense_120 (Dense)               │ (None, 12)             │         1,212 │\n",
        "└─────────────────────────────────┴────────────────────────┴───────────────┘\n",
        "
\n"
       ],
@@ -4272,11 +1868,11 @@
        "┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n",
        "┃\u001b[1m \u001b[0m\u001b[1mLayer (type)                   \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mOutput Shape          \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m      Param #\u001b[0m\u001b[1m \u001b[0m┃\n",
        "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n",
-       "│ lstm_40 (\u001b[38;5;33mLSTM\u001b[0m)                  │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m45\u001b[0m, \u001b[38;5;34m100\u001b[0m)        │        \u001b[38;5;34m48,000\u001b[0m │\n",
+       "│ tkan_80 (\u001b[38;5;33mTKAN\u001b[0m)                  │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m60\u001b[0m, \u001b[38;5;34m100\u001b[0m)        │        \u001b[38;5;34m41,316\u001b[0m │\n",
        "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
-       "│ lstm_41 (\u001b[38;5;33mLSTM\u001b[0m)                  │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m100\u001b[0m)            │        \u001b[38;5;34m80,400\u001b[0m │\n",
+       "│ tkan_81 (\u001b[38;5;33mTKAN\u001b[0m)                  │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m100\u001b[0m)            │        \u001b[38;5;34m67,670\u001b[0m │\n",
        "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
-       "│ dense_70 (\u001b[38;5;33mDense\u001b[0m)                │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m3\u001b[0m)              │           \u001b[38;5;34m303\u001b[0m │\n",
+       "│ dense_120 (\u001b[38;5;33mDense\u001b[0m)               │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m12\u001b[0m)             │         \u001b[38;5;34m1,212\u001b[0m │\n",
        "└─────────────────────────────────┴────────────────────────┴───────────────┘\n"
       ]
      },
@@ -4286,11 +1882,11 @@
     {
      "data": {
       "text/html": [
-       "
 Total params: 128,703 (502.75 KB)\n",
+       "
 Total params: 110,198 (430.46 KB)\n",
        "
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       ],
       "text/plain": [
-       "\u001b[1m Total params: \u001b[0m\u001b[38;5;34m128,703\u001b[0m (502.75 KB)\n"
+       "\u001b[1m Total params: \u001b[0m\u001b[38;5;34m110,198\u001b[0m (430.46 KB)\n"
       ]
      },
      "metadata": {},
@@ -4299,11 +1895,11 @@
     {
      "data": {
       "text/html": [
-       "
 Trainable params: 128,703 (502.75 KB)\n",
+       "
 Trainable params: 110,178 (430.38 KB)\n",
        "
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       ],
       "text/plain": [
-       "\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m128,703\u001b[0m (502.75 KB)\n"
+       "\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m110,178\u001b[0m (430.38 KB)\n"
       ]
      },
      "metadata": {},
@@ -4312,11 +1908,11 @@
     {
      "data": {
       "text/html": [
-       "
 Non-trainable params: 0 (0.00 B)\n",
+       "
 Non-trainable params: 20 (80.00 B)\n",
        "
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       "text/plain": [
-       "\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m0\u001b[0m (0.00 B)\n"
+       "\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m20\u001b[0m (80.00 B)\n"
       ]
      },
      "metadata": {},
@@ -4326,381 +1922,94 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "27.456106424331665 0.05745848167891302\n",
-      "25.54291844367981 0.04933120546474171\n",
-      "27.04043936729431 0.14269677898499963\n",
-      "24.304097175598145 0.16428378453298487\n",
-      "26.050607442855835 0.13263322060964924\n",
-      "R2 scores\n",
-      "Means:\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/usr/local/lib/python3.11/dist-packages/numpy/core/fromnumeric.py:3504: RuntimeWarning: Mean of empty slice.\n",
-      "  return _methods._mean(a, axis=axis, dtype=dtype,\n",
-      "/usr/local/lib/python3.11/dist-packages/numpy/core/_methods.py:129: RuntimeWarning: invalid value encountered in scalar divide\n",
-      "  ret = ret.dtype.type(ret / rcount)\n"
+      "102.03934359550476 0.0950019998069378\n",
+      "121.27882838249207 0.10264306639669311\n",
+      "108.56248140335083 0.0990784027938416\n",
+      "77.42332911491394 0.0915531316015012\n",
+      "108.50404167175293 0.09352464587821978\n",
+      "95.75039339065552 0.092634008105403\n",
+      "102.65606546401978 0.09820662504181471\n",
+      "130.8202440738678 0.09765221932195063\n",
+      "113.90040755271912 0.09989627016065132\n",
+      "118.27212023735046 0.1041245428645089\n"
      ]
     },
     {
      "data": {
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TKANGRULSTM
150.0932910.078430-0.231738
120.0960050.051663-0.362725
90.1115380.048128-0.123209
60.1322270.119756-0.004079
30.1929160.2377570.109281
1NaNNaNNaN
\n",
-       "
"
+       "
Model: \"GRU\"\n",
+       "
\n"
       ],
       "text/plain": [
-       "        TKAN       GRU      LSTM\n",
-       "15  0.093291  0.078430 -0.231738\n",
-       "12  0.096005  0.051663 -0.362725\n",
-       "9   0.111538  0.048128 -0.123209\n",
-       "6   0.132227  0.119756 -0.004079\n",
-       "3   0.192916  0.237757  0.109281\n",
-       "1        NaN       NaN       NaN"
+       "\u001b[1mModel: \"GRU\"\u001b[0m\n"
       ]
      },
      "metadata": {},
      "output_type": "display_data"
     },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/usr/local/lib/python3.11/dist-packages/numpy/core/fromnumeric.py:3504: RuntimeWarning: Mean of empty slice.\n",
-      "  return _methods._mean(a, axis=axis, dtype=dtype,\n",
-      "/usr/local/lib/python3.11/dist-packages/numpy/core/_methods.py:129: RuntimeWarning: invalid value encountered in scalar divide\n",
-      "  ret = ret.dtype.type(ret / rcount)\n"
-     ]
-    },
     {
      "data": {
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TKANGRULSTM
150.0677050.0681940.078686
120.0673210.0688310.082159
90.0667170.0688820.074797
60.0661310.0664700.070901
30.0635600.0616880.066594
1NaNNaNNaN
\n",
-       "
"
+       "
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n",
+       "┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃\n",
+       "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n",
+       "│ gru_80 (GRU)                    │ (None, 60, 100)        │        36,300 │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ gru_81 (GRU)                    │ (None, 100)            │        60,600 │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ dense_130 (Dense)               │ (None, 12)             │         1,212 │\n",
+       "└─────────────────────────────────┴────────────────────────┴───────────────┘\n",
+       "
\n"
       ],
       "text/plain": [
-       "        TKAN       GRU      LSTM\n",
-       "15  0.067705  0.068194  0.078686\n",
-       "12  0.067321  0.068831  0.082159\n",
-       "9   0.066717  0.068882  0.074797\n",
-       "6   0.066131  0.066470  0.070901\n",
-       "3   0.063560  0.061688  0.066594\n",
-       "1        NaN       NaN       NaN"
+       "┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n",
+       "┃\u001b[1m \u001b[0m\u001b[1mLayer (type)                   \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mOutput Shape          \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m      Param #\u001b[0m\u001b[1m \u001b[0m┃\n",
+       "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n",
+       "│ gru_80 (\u001b[38;5;33mGRU\u001b[0m)                    │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m60\u001b[0m, \u001b[38;5;34m100\u001b[0m)        │        \u001b[38;5;34m36,300\u001b[0m │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ gru_81 (\u001b[38;5;33mGRU\u001b[0m)                    │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m100\u001b[0m)            │        \u001b[38;5;34m60,600\u001b[0m │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ dense_130 (\u001b[38;5;33mDense\u001b[0m)               │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m12\u001b[0m)             │         \u001b[38;5;34m1,212\u001b[0m │\n",
+       "└─────────────────────────────────┴────────────────────────┴───────────────┘\n"
       ]
      },
      "metadata": {},
      "output_type": "display_data"
     },
-    {
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-     "text": [
-      "Std:\n"
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-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/usr/local/lib/python3.11/dist-packages/numpy/core/_methods.py:206: RuntimeWarning: Degrees of freedom <= 0 for slice\n",
-      "  ret = _var(a, axis=axis, dtype=dtype, out=out, ddof=ddof,\n",
-      "/usr/local/lib/python3.11/dist-packages/numpy/core/_methods.py:163: RuntimeWarning: invalid value encountered in divide\n",
-      "  arrmean = um.true_divide(arrmean, div, out=arrmean,\n",
-      "/usr/local/lib/python3.11/dist-packages/numpy/core/_methods.py:198: RuntimeWarning: invalid value encountered in scalar divide\n",
-      "  ret = ret.dtype.type(ret / rcount)\n"
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TKANGRULSTM
150.0055390.0159250.103254
120.0040600.0562630.219555
90.0034570.0607830.097549
60.0050740.0314590.076632
30.0020540.0028820.046833
1NaNNaNNaN
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-       "
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+       "
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+       "
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       ],
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-       "        TKAN       GRU      LSTM\n",
-       "15  0.005539  0.015925  0.103254\n",
-       "12  0.004060  0.056263  0.219555\n",
-       "9   0.003457  0.060783  0.097549\n",
-       "6   0.005074  0.031459  0.076632\n",
-       "3   0.002054  0.002882  0.046833\n",
-       "1        NaN       NaN       NaN"
+       "\u001b[1m Total params: \u001b[0m\u001b[38;5;34m98,112\u001b[0m (383.25 KB)\n"
       ]
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      "metadata": {},
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-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/usr/local/lib/python3.11/dist-packages/numpy/core/_methods.py:206: RuntimeWarning: Degrees of freedom <= 0 for slice\n",
-      "  ret = _var(a, axis=axis, dtype=dtype, out=out, ddof=ddof,\n",
-      "/usr/local/lib/python3.11/dist-packages/numpy/core/_methods.py:163: RuntimeWarning: invalid value encountered in divide\n",
-      "  arrmean = um.true_divide(arrmean, div, out=arrmean,\n",
-      "/usr/local/lib/python3.11/dist-packages/numpy/core/_methods.py:198: RuntimeWarning: invalid value encountered in scalar divide\n",
-      "  ret = ret.dtype.type(ret / rcount)\n"
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TKANGRULSTM
150.0002150.0005780.003284
120.0001480.0019780.006539
90.0001310.0021420.003162
60.0001990.0011510.002695
30.0000940.0001120.001722
1NaNNaNNaN
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-       "
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+       "
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+       "
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+      ],
+      "text/plain": [
+       "\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m98,112\u001b[0m (383.25 KB)\n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "
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+       "
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       ],
       "text/plain": [
-       "        TKAN       GRU      LSTM\n",
-       "15  0.000215  0.000578  0.003284\n",
-       "12  0.000148  0.001978  0.006539\n",
-       "9   0.000131  0.002142  0.003162\n",
-       "6   0.000199  0.001151  0.002695\n",
-       "3   0.000094  0.000112  0.001722\n",
-       "1        NaN       NaN       NaN"
+       "\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m0\u001b[0m (0.00 B)\n"
       ]
      },
      "metadata": {},
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      "name": "stdout",
      "output_type": "stream",
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-      "Training Times\n"
+      "35.47221755981445 0.04088546661722716\n",
+      "36.82418465614319 0.03298667710271531\n",
+      "29.090290307998657 0.08667586160493074\n",
+      "27.488890886306763 0.09733002632430683\n",
+      "30.277069330215454 0.10445165105456156\n",
+      "26.764411687850952 0.11400616888042837\n",
+      "35.36870241165161 0.02046360232907878\n",
+      "26.94648003578186 0.11067681945648022\n",
+      "35.716086864471436 0.02628936001538192\n",
+      "32.919156312942505 0.05842798641178421\n"
      ]
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-     "output_type": "stream",
-     "text": [
-      "/usr/local/lib/python3.11/dist-packages/numpy/core/fromnumeric.py:3504: RuntimeWarning: Mean of empty slice.\n",
-      "  return _methods._mean(a, axis=axis, dtype=dtype,\n",
-      "/usr/local/lib/python3.11/dist-packages/numpy/core/_methods.py:129: RuntimeWarning: invalid value encountered in scalar divide\n",
-      "  ret = ret.dtype.type(ret / rcount)\n"
-     ]
+     "data": {
+      "text/html": [
+       "
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+       "
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+      ],
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+       "\u001b[1mModel: \"LSTM\"\u001b[0m\n"
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TKANGRULSTM
15148.76306842.47829633.661120
12131.65323141.33398731.695352
9112.19363236.14423324.899039
697.35147739.04863226.407373
388.30683634.37949226.078834
1NaNNaNNaN
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+       "
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n",
+       "┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃\n",
+       "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n",
+       "│ lstm_80 (LSTM)                  │ (None, 60, 100)        │        48,000 │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ lstm_81 (LSTM)                  │ (None, 100)            │        80,400 │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ dense_140 (Dense)               │ (None, 12)             │         1,212 │\n",
+       "└─────────────────────────────────┴────────────────────────┴───────────────┘\n",
+       "
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       ],
       "text/plain": [
-       "          TKAN        GRU       LSTM\n",
-       "15  148.763068  42.478296  33.661120\n",
-       "12  131.653231  41.333987  31.695352\n",
-       "9   112.193632  36.144233  24.899039\n",
-       "6    97.351477  39.048632  26.407373\n",
-       "3    88.306836  34.379492  26.078834\n",
-       "1          NaN        NaN        NaN"
+       "┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n",
+       "┃\u001b[1m \u001b[0m\u001b[1mLayer (type)                   \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mOutput Shape          \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m      Param #\u001b[0m\u001b[1m \u001b[0m┃\n",
+       "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n",
+       "│ lstm_80 (\u001b[38;5;33mLSTM\u001b[0m)                  │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m60\u001b[0m, \u001b[38;5;34m100\u001b[0m)        │        \u001b[38;5;34m48,000\u001b[0m │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ lstm_81 (\u001b[38;5;33mLSTM\u001b[0m)                  │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m100\u001b[0m)            │        \u001b[38;5;34m80,400\u001b[0m │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ dense_140 (\u001b[38;5;33mDense\u001b[0m)               │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m12\u001b[0m)             │         \u001b[38;5;34m1,212\u001b[0m │\n",
+       "└─────────────────────────────────┴────────────────────────┴───────────────┘\n"
       ]
      },
      "metadata": {},
      "output_type": "display_data"
     },
     {
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-     "output_type": "stream",
-     "text": [
-      "/usr/local/lib/python3.11/dist-packages/numpy/core/_methods.py:206: RuntimeWarning: Degrees of freedom <= 0 for slice\n",
-      "  ret = _var(a, axis=axis, dtype=dtype, out=out, ddof=ddof,\n",
-      "/usr/local/lib/python3.11/dist-packages/numpy/core/_methods.py:163: RuntimeWarning: invalid value encountered in divide\n",
-      "  arrmean = um.true_divide(arrmean, div, out=arrmean,\n",
-      "/usr/local/lib/python3.11/dist-packages/numpy/core/_methods.py:198: RuntimeWarning: invalid value encountered in scalar divide\n",
-      "  ret = ret.dtype.type(ret / rcount)\n"
-     ]
+     "data": {
+      "text/html": [
+       "
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+       "
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+      ],
+      "text/plain": [
+       "\u001b[1m Total params: \u001b[0m\u001b[38;5;34m129,612\u001b[0m (506.30 KB)\n"
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TKANGRULSTM
1514.6747052.7050711.335022
126.6137834.4866613.014970
916.3547424.6898431.760482
613.0052653.4739113.841358
38.1179942.3753971.118862
1NaNNaNNaN
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+       "
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+       "
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       ],
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-       "         TKAN       GRU      LSTM\n",
-       "15  14.674705  2.705071  1.335022\n",
-       "12   6.613783  4.486661  3.014970\n",
-       "9   16.354742  4.689843  1.760482\n",
-       "6   13.005265  3.473911  3.841358\n",
-       "3    8.117994  2.375397  1.118862\n",
-       "1         NaN       NaN       NaN"
+       "\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m129,612\u001b[0m (506.30 KB)\n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "
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+       "
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+      ],
+      "text/plain": [
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+      "26.069539546966553 -0.36695473176783655\n",
+      "21.807262659072876 -0.25014631415290206\n",
+      "20.902458667755127 -0.11645941888847307\n",
+      "22.4891197681427 -0.052407595508665694\n",
+      "22.018208742141724 -0.1754222997379895\n",
+      "23.925899028778076 -0.444045746274295\n",
+      "23.6626615524292 -0.2362643247514915\n",
+      "20.98034143447876 -0.16545529402468354\n",
+      "22.008980751037598 -0.23959377820038377\n",
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┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n",
        "┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃\n",
        "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n",
-       "│ tkan_50 (TKAN)                  │ (None, 45, 100)        │        41,750 │\n",
+       "│ tkan_100 (TKAN)                 │ (None, 75, 100)        │        41,316 │\n",
        "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
-       "│ tkan_51 (TKAN)                  │ (None, 100)            │        67,670 │\n",
+       "│ tkan_101 (TKAN)                 │ (None, 100)            │        67,670 │\n",
        "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
-       "│ dense_75 (Dense)                │ (None, 1)              │           101 │\n",
+       "│ dense_150 (Dense)               │ (None, 15)             │         1,515 │\n",
        "└─────────────────────────────────┴────────────────────────┴───────────────┘\n",
        "
\n"
       ],
@@ -4926,11 +2159,11 @@
        "┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n",
        "┃\u001b[1m \u001b[0m\u001b[1mLayer (type)                   \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mOutput Shape          \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m      Param #\u001b[0m\u001b[1m \u001b[0m┃\n",
        "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n",
-       "│ tkan_50 (\u001b[38;5;33mTKAN\u001b[0m)                  │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m45\u001b[0m, \u001b[38;5;34m100\u001b[0m)        │        \u001b[38;5;34m41,750\u001b[0m │\n",
+       "│ tkan_100 (\u001b[38;5;33mTKAN\u001b[0m)                 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m75\u001b[0m, \u001b[38;5;34m100\u001b[0m)        │        \u001b[38;5;34m41,316\u001b[0m │\n",
        "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
-       "│ tkan_51 (\u001b[38;5;33mTKAN\u001b[0m)                  │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m100\u001b[0m)            │        \u001b[38;5;34m67,670\u001b[0m │\n",
+       "│ tkan_101 (\u001b[38;5;33mTKAN\u001b[0m)                 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m100\u001b[0m)            │        \u001b[38;5;34m67,670\u001b[0m │\n",
        "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
-       "│ dense_75 (\u001b[38;5;33mDense\u001b[0m)                │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m)              │           \u001b[38;5;34m101\u001b[0m │\n",
+       "│ dense_150 (\u001b[38;5;33mDense\u001b[0m)               │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m15\u001b[0m)             │         \u001b[38;5;34m1,515\u001b[0m │\n",
        "└─────────────────────────────────┴────────────────────────┴───────────────┘\n"
       ]
      },
@@ -4940,11 +2173,11 @@
     {
      "data": {
       "text/html": [
-       "
 Total params: 109,521 (427.82 KB)\n",
+       "
 Total params: 110,501 (431.64 KB)\n",
        "
\n"
       ],
       "text/plain": [
-       "\u001b[1m Total params: \u001b[0m\u001b[38;5;34m109,521\u001b[0m (427.82 KB)\n"
+       "\u001b[1m Total params: \u001b[0m\u001b[38;5;34m110,501\u001b[0m (431.64 KB)\n"
       ]
      },
      "metadata": {},
@@ -4953,11 +2186,11 @@
     {
      "data": {
       "text/html": [
-       "
 Trainable params: 109,501 (427.74 KB)\n",
+       "
 Trainable params: 110,481 (431.57 KB)\n",
        "
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       ],
       "text/plain": [
-       "\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m109,501\u001b[0m (427.74 KB)\n"
+       "\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m110,481\u001b[0m (431.57 KB)\n"
       ]
      },
      "metadata": {},
@@ -4980,11 +2213,16 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "81.33754324913025 0.3022592965618307\n",
-      "104.95103526115417 0.302846112010449\n",
-      "111.78685140609741 0.33146529199419217\n",
-      "64.58520579338074 0.27988703845286933\n",
-      "115.72550010681152 0.32013992233948596\n"
+      "108.7913281917572 0.09994023646490877\n",
+      "156.19587421417236 0.08586536769033742\n",
+      "151.76872277259827 0.0953234353911566\n",
+      "109.25939583778381 0.08350316914250061\n",
+      "120.67966675758362 0.09383066359946372\n",
+      "118.93469595909119 0.09027677327433754\n",
+      "117.88046956062317 0.09486514143615367\n",
+      "106.22660422325134 0.09703190398065814\n",
+      "154.6233696937561 0.09741707472392\n",
+      "124.99523186683655 0.09227563879811171\n"
      ]
     },
     {
@@ -5006,11 +2244,11 @@
        "
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n",
        "┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃\n",
        "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n",
-       "│ gru_50 (GRU)                    │ (None, 45, 100)        │        36,300 │\n",
+       "│ gru_100 (GRU)                   │ (None, 75, 100)        │        36,300 │\n",
        "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
-       "│ gru_51 (GRU)                    │ (None, 100)            │        60,600 │\n",
+       "│ gru_101 (GRU)                   │ (None, 100)            │        60,600 │\n",
        "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
-       "│ dense_80 (Dense)                │ (None, 1)              │           101 │\n",
+       "│ dense_160 (Dense)               │ (None, 15)             │         1,515 │\n",
        "└─────────────────────────────────┴────────────────────────┴───────────────┘\n",
        "
\n"
       ],
@@ -5018,11 +2256,11 @@
        "┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n",
        "┃\u001b[1m \u001b[0m\u001b[1mLayer (type)                   \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mOutput Shape          \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m      Param #\u001b[0m\u001b[1m \u001b[0m┃\n",
        "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n",
-       "│ gru_50 (\u001b[38;5;33mGRU\u001b[0m)                    │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m45\u001b[0m, \u001b[38;5;34m100\u001b[0m)        │        \u001b[38;5;34m36,300\u001b[0m │\n",
+       "│ gru_100 (\u001b[38;5;33mGRU\u001b[0m)                   │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m75\u001b[0m, \u001b[38;5;34m100\u001b[0m)        │        \u001b[38;5;34m36,300\u001b[0m │\n",
        "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
-       "│ gru_51 (\u001b[38;5;33mGRU\u001b[0m)                    │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m100\u001b[0m)            │        \u001b[38;5;34m60,600\u001b[0m │\n",
+       "│ gru_101 (\u001b[38;5;33mGRU\u001b[0m)                   │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m100\u001b[0m)            │        \u001b[38;5;34m60,600\u001b[0m │\n",
        "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
-       "│ dense_80 (\u001b[38;5;33mDense\u001b[0m)                │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m)              │           \u001b[38;5;34m101\u001b[0m │\n",
+       "│ dense_160 (\u001b[38;5;33mDense\u001b[0m)               │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m15\u001b[0m)             │         \u001b[38;5;34m1,515\u001b[0m │\n",
        "└─────────────────────────────────┴────────────────────────┴───────────────┘\n"
       ]
      },
@@ -5032,11 +2270,11 @@
     {
      "data": {
       "text/html": [
-       "
 Total params: 97,001 (378.91 KB)\n",
+       "
 Total params: 98,415 (384.43 KB)\n",
        "
\n"
       ],
       "text/plain": [
-       "\u001b[1m Total params: \u001b[0m\u001b[38;5;34m97,001\u001b[0m (378.91 KB)\n"
+       "\u001b[1m Total params: \u001b[0m\u001b[38;5;34m98,415\u001b[0m (384.43 KB)\n"
       ]
      },
      "metadata": {},
@@ -5045,11 +2283,11 @@
     {
      "data": {
       "text/html": [
-       "
 Trainable params: 97,001 (378.91 KB)\n",
+       "
 Trainable params: 98,415 (384.43 KB)\n",
        "
\n"
       ],
       "text/plain": [
-       "\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m97,001\u001b[0m (378.91 KB)\n"
+       "\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m98,415\u001b[0m (384.43 KB)\n"
       ]
      },
      "metadata": {},
@@ -5072,11 +2310,16 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "42.188021659851074 0.39814672018793895\n",
-      "32.78909373283386 0.3956099221775101\n",
-      "35.24668765068054 0.3982762017641819\n",
-      "42.99339699745178 0.3956046326974988\n",
-      "28.0660617351532 0.3989501248082734\n"
+      "41.711098432540894 0.04633579312704695\n",
+      "37.61102104187012 0.041959092296909146\n",
+      "33.6794171333313 0.08124711803653464\n",
+      "41.39418888092041 0.062273110060791455\n",
+      "33.090798139572144 0.09644091868374302\n",
+      "34.905259132385254 0.0688485348099919\n",
+      "31.54578924179077 0.09070480771844083\n",
+      "31.649243354797363 0.08222696375881307\n",
+      "34.72465991973877 0.07408738048462013\n",
+      "34.364349365234375 0.06764429477726015\n"
      ]
     },
     {
@@ -5098,11 +2341,11 @@
        "
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n",
        "┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃\n",
        "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n",
-       "│ lstm_50 (LSTM)                  │ (None, 45, 100)        │        48,000 │\n",
+       "│ lstm_100 (LSTM)                 │ (None, 75, 100)        │        48,000 │\n",
        "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
-       "│ lstm_51 (LSTM)                  │ (None, 100)            │        80,400 │\n",
+       "│ lstm_101 (LSTM)                 │ (None, 100)            │        80,400 │\n",
        "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
-       "│ dense_85 (Dense)                │ (None, 1)              │           101 │\n",
+       "│ dense_170 (Dense)               │ (None, 15)             │         1,515 │\n",
        "└─────────────────────────────────┴────────────────────────┴───────────────┘\n",
        "
\n"
       ],
@@ -5110,11 +2353,11 @@
        "┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n",
        "┃\u001b[1m \u001b[0m\u001b[1mLayer (type)                   \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mOutput Shape          \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m      Param #\u001b[0m\u001b[1m \u001b[0m┃\n",
        "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n",
-       "│ lstm_50 (\u001b[38;5;33mLSTM\u001b[0m)                  │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m45\u001b[0m, \u001b[38;5;34m100\u001b[0m)        │        \u001b[38;5;34m48,000\u001b[0m │\n",
+       "│ lstm_100 (\u001b[38;5;33mLSTM\u001b[0m)                 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m75\u001b[0m, \u001b[38;5;34m100\u001b[0m)        │        \u001b[38;5;34m48,000\u001b[0m │\n",
        "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
-       "│ lstm_51 (\u001b[38;5;33mLSTM\u001b[0m)                  │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m100\u001b[0m)            │        \u001b[38;5;34m80,400\u001b[0m │\n",
+       "│ lstm_101 (\u001b[38;5;33mLSTM\u001b[0m)                 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m100\u001b[0m)            │        \u001b[38;5;34m80,400\u001b[0m │\n",
        "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
-       "│ dense_85 (\u001b[38;5;33mDense\u001b[0m)                │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m)              │           \u001b[38;5;34m101\u001b[0m │\n",
+       "│ dense_170 (\u001b[38;5;33mDense\u001b[0m)               │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m15\u001b[0m)             │         \u001b[38;5;34m1,515\u001b[0m │\n",
        "└─────────────────────────────────┴────────────────────────┴───────────────┘\n"
       ]
      },
@@ -5124,11 +2367,11 @@
     {
      "data": {
       "text/html": [
-       "
 Total params: 128,501 (501.96 KB)\n",
+       "
 Total params: 129,915 (507.48 KB)\n",
        "
\n"
       ],
       "text/plain": [
-       "\u001b[1m Total params: \u001b[0m\u001b[38;5;34m128,501\u001b[0m (501.96 KB)\n"
+       "\u001b[1m Total params: \u001b[0m\u001b[38;5;34m129,915\u001b[0m (507.48 KB)\n"
       ]
      },
      "metadata": {},
@@ -5137,11 +2380,11 @@
     {
      "data": {
       "text/html": [
-       "
 Trainable params: 128,501 (501.96 KB)\n",
+       "
 Trainable params: 129,915 (507.48 KB)\n",
        "
\n"
       ],
       "text/plain": [
-       "\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m128,501\u001b[0m (501.96 KB)\n"
+       "\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m129,915\u001b[0m (507.48 KB)\n"
       ]
      },
      "metadata": {},
@@ -5164,11 +2407,16 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "25.248199701309204 0.3954618995452197\n",
-      "25.895612478256226 0.36461360004863774\n",
-      "22.943801403045654 0.39227780049103544\n",
-      "25.566108465194702 0.3725527679003343\n",
-      "24.88447666168213 0.3704866575757282\n",
+      "27.019399642944336 -0.02014558059192651\n",
+      "24.994324684143066 -0.034652205897714984\n",
+      "29.875847578048706 -0.2883331388024311\n",
+      "27.68077278137207 -0.19307423134624493\n",
+      "32.10626721382141 -0.1967002645401843\n",
+      "26.022549629211426 -0.10374175022709843\n",
+      "28.117710828781128 -0.0705739546737177\n",
+      "30.498241662979126 -0.25929543857022325\n",
+      "24.907368898391724 -0.09258147039272031\n",
+      "26.723052978515625 -0.15915120445580425\n",
       "R2 scores\n",
       "Means:\n"
      ]
@@ -5201,40 +2449,40 @@
        "  \n",
        "  \n",
        "    \n",
-       "      15\n",
-       "      0.093291\n",
-       "      0.078430\n",
-       "      -0.231738\n",
+       "      1\n",
+       "      0.310078\n",
+       "      0.394863\n",
+       "      0.381780\n",
        "    \n",
        "    \n",
-       "      12\n",
-       "      0.096005\n",
-       "      0.051663\n",
-       "      -0.362725\n",
+       "      3\n",
+       "      0.190549\n",
+       "      0.236880\n",
+       "      0.113571\n",
        "    \n",
        "    \n",
-       "      9\n",
-       "      0.111538\n",
-       "      0.048128\n",
-       "      -0.123209\n",
+       "      6\n",
+       "      0.128743\n",
+       "      0.129840\n",
+       "      -0.045114\n",
        "    \n",
        "    \n",
-       "      6\n",
-       "      0.132227\n",
-       "      0.119756\n",
-       "      -0.004079\n",
+       "      9\n",
+       "      0.107099\n",
+       "      0.067396\n",
+       "      -0.186374\n",
        "    \n",
        "    \n",
-       "      3\n",
-       "      0.192916\n",
-       "      0.237757\n",
-       "      0.109281\n",
+       "      12\n",
+       "      0.097431\n",
+       "      0.069219\n",
+       "      -0.254701\n",
        "    \n",
        "    \n",
-       "      1\n",
-       "      0.307320\n",
-       "      0.397318\n",
-       "      0.379079\n",
+       "      15\n",
+       "      0.093033\n",
+       "      0.071177\n",
+       "      -0.141825\n",
        "    \n",
        "  \n",
        "\n",
@@ -5242,12 +2490,12 @@
       ],
       "text/plain": [
        "        TKAN       GRU      LSTM\n",
-       "15  0.093291  0.078430 -0.231738\n",
-       "12  0.096005  0.051663 -0.362725\n",
-       "9   0.111538  0.048128 -0.123209\n",
-       "6   0.132227  0.119756 -0.004079\n",
-       "3   0.192916  0.237757  0.109281\n",
-       "1   0.307320  0.397318  0.379079"
+       "1   0.310078  0.394863  0.381780\n",
+       "3   0.190549  0.236880  0.113571\n",
+       "6   0.128743  0.129840 -0.045114\n",
+       "9   0.107099  0.067396 -0.186374\n",
+       "12  0.097431  0.069219 -0.254701\n",
+       "15  0.093033  0.071177 -0.141825"
       ]
      },
      "metadata": {},
@@ -5281,40 +2529,40 @@
        "  \n",
        "  \n",
        "    \n",
-       "      15\n",
-       "      0.067705\n",
-       "      0.068194\n",
-       "      0.078686\n",
+       "      1\n",
+       "      0.058833\n",
+       "      0.055101\n",
+       "      0.055693\n",
        "    \n",
        "    \n",
-       "      12\n",
-       "      0.067321\n",
-       "      0.068831\n",
-       "      0.082159\n",
+       "      3\n",
+       "      0.063659\n",
+       "      0.061720\n",
+       "      0.066402\n",
        "    \n",
        "    \n",
-       "      9\n",
-       "      0.066717\n",
-       "      0.068882\n",
-       "      0.074797\n",
+       "      6\n",
+       "      0.066264\n",
+       "      0.066109\n",
+       "      0.072302\n",
        "    \n",
        "    \n",
-       "      6\n",
-       "      0.066131\n",
-       "      0.066470\n",
-       "      0.070901\n",
+       "      9\n",
+       "      0.066893\n",
+       "      0.068206\n",
+       "      0.076763\n",
        "    \n",
        "    \n",
-       "      3\n",
-       "      0.063560\n",
-       "      0.061688\n",
-       "      0.066594\n",
+       "      12\n",
+       "      0.067267\n",
+       "      0.068214\n",
+       "      0.079032\n",
        "    \n",
        "    \n",
-       "      1\n",
-       "      0.058948\n",
-       "      0.054990\n",
-       "      0.055813\n",
+       "      15\n",
+       "      0.067711\n",
+       "      0.068456\n",
+       "      0.075833\n",
        "    \n",
        "  \n",
        "\n",
@@ -5322,12 +2570,12 @@
       ],
       "text/plain": [
        "        TKAN       GRU      LSTM\n",
-       "15  0.067705  0.068194  0.078686\n",
-       "12  0.067321  0.068831  0.082159\n",
-       "9   0.066717  0.068882  0.074797\n",
-       "6   0.066131  0.066470  0.070901\n",
-       "3   0.063560  0.061688  0.066594\n",
-       "1   0.058948  0.054990  0.055813"
+       "1   0.058833  0.055101  0.055693\n",
+       "3   0.063659  0.061720  0.066402\n",
+       "6   0.066264  0.066109  0.072302\n",
+       "9   0.066893  0.068206  0.076763\n",
+       "12  0.067267  0.068214  0.079032\n",
+       "15  0.067711  0.068456  0.075833"
       ]
      },
      "metadata": {},
@@ -5368,40 +2616,40 @@
        "  \n",
        "  \n",
        "    \n",
-       "      15\n",
-       "      0.005539\n",
-       "      0.015925\n",
-       "      0.103254\n",
+       "      1\n",
+       "      0.013617\n",
+       "      0.007498\n",
+       "      0.009371\n",
        "    \n",
        "    \n",
-       "      12\n",
-       "      0.004060\n",
-       "      0.056263\n",
-       "      0.219555\n",
+       "      3\n",
+       "      0.007820\n",
+       "      0.006761\n",
+       "      0.063202\n",
        "    \n",
        "    \n",
-       "      9\n",
-       "      0.003457\n",
-       "      0.060783\n",
-       "      0.097549\n",
+       "      6\n",
+       "      0.008831\n",
+       "      0.018997\n",
+       "      0.112792\n",
        "    \n",
        "    \n",
-       "      6\n",
-       "      0.005074\n",
-       "      0.031459\n",
-       "      0.076632\n",
+       "      9\n",
+       "      0.006202\n",
+       "      0.057011\n",
+       "      0.156331\n",
        "    \n",
        "    \n",
-       "      3\n",
-       "      0.002054\n",
-       "      0.002882\n",
-       "      0.046833\n",
+       "      12\n",
+       "      0.004002\n",
+       "      0.035374\n",
+       "      0.135507\n",
        "    \n",
        "    \n",
-       "      1\n",
-       "      0.017581\n",
-       "      0.001423\n",
-       "      0.012396\n",
+       "      15\n",
+       "      0.004925\n",
+       "      0.016791\n",
+       "      0.087433\n",
        "    \n",
        "  \n",
        "\n",
@@ -5409,12 +2657,12 @@
       ],
       "text/plain": [
        "        TKAN       GRU      LSTM\n",
-       "15  0.005539  0.015925  0.103254\n",
-       "12  0.004060  0.056263  0.219555\n",
-       "9   0.003457  0.060783  0.097549\n",
-       "6   0.005074  0.031459  0.076632\n",
-       "3   0.002054  0.002882  0.046833\n",
-       "1   0.017581  0.001423  0.012396"
+       "1   0.013617  0.007498  0.009371\n",
+       "3   0.007820  0.006761  0.063202\n",
+       "6   0.008831  0.018997  0.112792\n",
+       "9   0.006202  0.057011  0.156331\n",
+       "12  0.004002  0.035374  0.135507\n",
+       "15  0.004925  0.016791  0.087433"
       ]
      },
      "metadata": {},
@@ -5448,40 +2696,40 @@
        "  \n",
        "  \n",
        "    \n",
-       "      15\n",
-       "      0.000215\n",
-       "      0.000578\n",
-       "      0.003284\n",
+       "      1\n",
+       "      0.000581\n",
+       "      0.000340\n",
+       "      0.000421\n",
        "    \n",
        "    \n",
-       "      12\n",
-       "      0.000148\n",
-       "      0.001978\n",
-       "      0.006539\n",
+       "      3\n",
+       "      0.000316\n",
+       "      0.000273\n",
+       "      0.002282\n",
        "    \n",
        "    \n",
-       "      9\n",
-       "      0.000131\n",
-       "      0.002142\n",
-       "      0.003162\n",
+       "      6\n",
+       "      0.000347\n",
+       "      0.000707\n",
+       "      0.003831\n",
        "    \n",
        "    \n",
-       "      6\n",
-       "      0.000199\n",
-       "      0.001151\n",
-       "      0.002695\n",
+       "      9\n",
+       "      0.000237\n",
+       "      0.002033\n",
+       "      0.004908\n",
        "    \n",
        "    \n",
-       "      3\n",
-       "      0.000094\n",
-       "      0.000112\n",
-       "      0.001722\n",
+       "      12\n",
+       "      0.000157\n",
+       "      0.001278\n",
+       "      0.004174\n",
        "    \n",
        "    \n",
-       "      1\n",
-       "      0.000747\n",
-       "      0.000065\n",
-       "      0.000558\n",
+       "      15\n",
+       "      0.000187\n",
+       "      0.000609\n",
+       "      0.002844\n",
        "    \n",
        "  \n",
        "\n",
@@ -5489,12 +2737,12 @@
       ],
       "text/plain": [
        "        TKAN       GRU      LSTM\n",
-       "15  0.000215  0.000578  0.003284\n",
-       "12  0.000148  0.001978  0.006539\n",
-       "9   0.000131  0.002142  0.003162\n",
-       "6   0.000199  0.001151  0.002695\n",
-       "3   0.000094  0.000112  0.001722\n",
-       "1   0.000747  0.000065  0.000558"
+       "1   0.000581  0.000340  0.000421\n",
+       "3   0.000316  0.000273  0.002282\n",
+       "6   0.000347  0.000707  0.003831\n",
+       "9   0.000237  0.002033  0.004908\n",
+       "12  0.000157  0.001278  0.004174\n",
+       "15  0.000187  0.000609  0.002844"
       ]
      },
      "metadata": {},
@@ -5535,40 +2783,40 @@
        "  \n",
        "  \n",
        "    \n",
-       "      15\n",
-       "      148.763068\n",
-       "      42.478296\n",
-       "      33.661120\n",
+       "      1\n",
+       "      76.861277\n",
+       "      26.988356\n",
+       "      18.895929\n",
        "    \n",
        "    \n",
-       "      12\n",
-       "      131.653231\n",
-       "      41.333987\n",
-       "      31.695352\n",
+       "      3\n",
+       "      78.182759\n",
+       "      28.601693\n",
+       "      20.284139\n",
        "    \n",
        "    \n",
-       "      9\n",
-       "      112.193632\n",
-       "      36.144233\n",
-       "      24.899039\n",
+       "      6\n",
+       "      77.580002\n",
+       "      28.549838\n",
+       "      19.617023\n",
        "    \n",
        "    \n",
-       "      6\n",
-       "      97.351477\n",
-       "      39.048632\n",
-       "      26.407373\n",
+       "      9\n",
+       "      84.431719\n",
+       "      28.048108\n",
+       "      19.591124\n",
        "    \n",
        "    \n",
-       "      3\n",
-       "      88.306836\n",
-       "      34.379492\n",
-       "      26.078834\n",
+       "      12\n",
+       "      107.920725\n",
+       "      31.686749\n",
+       "      22.577694\n",
        "    \n",
        "    \n",
-       "      1\n",
-       "      95.677227\n",
-       "      36.256652\n",
-       "      24.907640\n",
+       "      15\n",
+       "      126.935536\n",
+       "      35.467582\n",
+       "      27.794554\n",
        "    \n",
        "  \n",
        "\n",
@@ -5576,12 +2824,12 @@
       ],
       "text/plain": [
        "          TKAN        GRU       LSTM\n",
-       "15  148.763068  42.478296  33.661120\n",
-       "12  131.653231  41.333987  31.695352\n",
-       "9   112.193632  36.144233  24.899039\n",
-       "6    97.351477  39.048632  26.407373\n",
-       "3    88.306836  34.379492  26.078834\n",
-       "1    95.677227  36.256652  24.907640"
+       "1    76.861277  26.988356  18.895929\n",
+       "3    78.182759  28.601693  20.284139\n",
+       "6    77.580002  28.549838  19.617023\n",
+       "9    84.431719  28.048108  19.591124\n",
+       "12  107.920725  31.686749  22.577694\n",
+       "15  126.935536  35.467582  27.794554"
       ]
      },
      "metadata": {},
@@ -5615,40 +2863,40 @@
        "  \n",
        "  \n",
        "    \n",
-       "      15\n",
-       "      14.674705\n",
-       "      2.705071\n",
-       "      1.335022\n",
+       "      1\n",
+       "      11.082262\n",
+       "      3.945290\n",
+       "      1.723379\n",
        "    \n",
        "    \n",
-       "      12\n",
-       "      6.613783\n",
-       "      4.486661\n",
-       "      3.014970\n",
+       "      3\n",
+       "      10.159973\n",
+       "      3.052013\n",
+       "      1.950043\n",
        "    \n",
        "    \n",
-       "      9\n",
-       "      16.354742\n",
-       "      4.689843\n",
-       "      1.760482\n",
+       "      6\n",
+       "      8.965218\n",
+       "      1.885116\n",
+       "      2.320250\n",
        "    \n",
        "    \n",
-       "      6\n",
-       "      13.005265\n",
-       "      3.473911\n",
-       "      3.841358\n",
+       "      9\n",
+       "      14.904037\n",
+       "      4.894200\n",
+       "      0.870263\n",
        "    \n",
        "    \n",
-       "      3\n",
-       "      8.117994\n",
-       "      2.375397\n",
-       "      1.118862\n",
+       "      12\n",
+       "      14.096107\n",
+       "      3.808997\n",
+       "      1.490844\n",
        "    \n",
        "    \n",
-       "      1\n",
-       "      19.594882\n",
-       "      5.669121\n",
-       "      1.037579\n",
+       "      15\n",
+       "      18.705339\n",
+       "      3.458216\n",
+       "      2.267511\n",
        "    \n",
        "  \n",
        "\n",
@@ -5656,12 +2904,12 @@
       ],
       "text/plain": [
        "         TKAN       GRU      LSTM\n",
-       "15  14.674705  2.705071  1.335022\n",
-       "12   6.613783  4.486661  3.014970\n",
-       "9   16.354742  4.689843  1.760482\n",
-       "6   13.005265  3.473911  3.841358\n",
-       "3    8.117994  2.375397  1.118862\n",
-       "1   19.594882  5.669121  1.037579"
+       "1   11.082262  3.945290  1.723379\n",
+       "3   10.159973  3.052013  1.950043\n",
+       "6    8.965218  1.885116  2.320250\n",
+       "9   14.904037  4.894200  0.870263\n",
+       "12  14.096107  3.808997  1.490844\n",
+       "15  18.705339  3.458216  2.267511"
       ]
      },
      "metadata": {},
@@ -5669,7 +2917,7 @@
     }
    ],
    "source": [
-    "n_aheads = [1, 3, 6, 9, 12, 15][::-1]\n",
+    "n_aheads = [1, 3, 6, 9, 12, 15]\n",
     "models = [\n",
     "    \"TKAN\",\n",
     "    \"GRU\",\n",
@@ -5685,12 +2933,12 @@
     "    \n",
     "    for model_id in models:\n",
     "        \n",
-    "        for run in range(5):\n",
+    "        for run in range(10):\n",
     "\n",
     "            if model_id == 'TKAN':\n",
     "                model = Sequential([\n",
     "                    Input(shape=X_train.shape[1:]),\n",
-    "                    TKAN(100, sub_kan_output_dim = 20, sub_kan_input_dim = 20, return_sequences=True),\n",
+    "                    TKAN(100, return_sequences=True),\n",
     "                    TKAN(100, sub_kan_output_dim = 20, sub_kan_input_dim = 20, return_sequences=False),\n",
     "                    Dense(units=n_ahead, activation='linear')\n",
     "                ], name = model_id)\n",
@@ -5712,7 +2960,7 @@
     "                raise ValueError\n",
     "            \n",
     "            optimizer = keras.optimizers.Adam(0.001)\n",
-    "            model.compile(optimizer=optimizer, loss='mean_squared_error')\n",
+    "            model.compile(optimizer=optimizer, loss='mean_squared_error', jit_compile=True)\n",
     "            if run==0:\n",
     "                model.summary()\n",
     "                \n",
@@ -5732,26 +2980,18 @@
     "            del model\n",
     "            del optimizer\n",
     "                \n",
-    "  \n",
-    "    print('R2 scores')\n",
-    "    print('Means:')\n",
-    "    display(pd.DataFrame({model_id: {n_ahead: np.mean(results[model_id][n_ahead]) for n_ahead in n_aheads} for model_id in results.keys()}))\n",
-    "    display(pd.DataFrame({model_id: {n_ahead: np.mean(results_rmse[model_id][n_ahead]) for n_ahead in n_aheads} for model_id in results_rmse.keys()}))\n",
-    "    print('Std:')\n",
-    "    display(pd.DataFrame({model_id: {n_ahead: np.std(results[model_id][n_ahead]) for n_ahead in n_aheads} for model_id in results.keys()}))\n",
-    "    display(pd.DataFrame({model_id: {n_ahead: np.std(results_rmse[model_id][n_ahead]) for n_ahead in n_aheads} for model_id in results_rmse.keys()}))\n",
-    "    print('Training Times')\n",
-    "    display(pd.DataFrame({model_id: {n_ahead: np.mean(time_results[model_id][n_ahead]) for n_ahead in n_aheads} for model_id in time_results.keys()}))\n",
-    "    display(pd.DataFrame({model_id: {n_ahead: np.std(time_results[model_id][n_ahead]) for n_ahead in n_aheads} for model_id in time_results.keys()}))"
+    "\n",
+    "print('R2 scores')\n",
+    "print('Means:')\n",
+    "display(pd.DataFrame({model_id: {n_ahead: np.mean(results[model_id][n_ahead]) for n_ahead in n_aheads} for model_id in results.keys()}))\n",
+    "display(pd.DataFrame({model_id: {n_ahead: np.mean(results_rmse[model_id][n_ahead]) for n_ahead in n_aheads} for model_id in results_rmse.keys()}))\n",
+    "print('Std:')\n",
+    "display(pd.DataFrame({model_id: {n_ahead: np.std(results[model_id][n_ahead]) for n_ahead in n_aheads} for model_id in results.keys()}))\n",
+    "display(pd.DataFrame({model_id: {n_ahead: np.std(results_rmse[model_id][n_ahead]) for n_ahead in n_aheads} for model_id in results_rmse.keys()}))\n",
+    "print('Training Times')\n",
+    "display(pd.DataFrame({model_id: {n_ahead: np.mean(time_results[model_id][n_ahead]) for n_ahead in n_aheads} for model_id in time_results.keys()}))\n",
+    "display(pd.DataFrame({model_id: {n_ahead: np.std(time_results[model_id][n_ahead]) for n_ahead in n_aheads} for model_id in time_results.keys()}))"
    ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "8ffccbbf-8ca4-4166-97bc-24c0f5ca02d0",
-   "metadata": {},
-   "outputs": [],
-   "source": []
   }
  ],
  "metadata": {
@@ -5770,7 +3010,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.12.2"
+   "version": "3.11.9"
   }
  },
  "nbformat": 4,