Allow direct initialization of NBeats model without from_dataset

[MeherBhaskar]

Description

This PR fixes issue #1548 where the NBeats model fails when initialized without using the from_dataset method. The changes enable direct initialization while maintaining backward compatibility and adding proper validation.

Changes

• Enhanced __init__ method with comprehensive parameter validation
• Added clear documentation for both initialization methods (direct and from_dataset)
• Added validation for parameter list lengths to match stack types
• Updated from_dataset method documentation to clarify its role
• Added unit tests for direct initialization

Now users can:

1. Use from_dataset (recommended) for standard time series forecasting
2. Initialize directly for custom use cases
3. Get clear error messages when parameters are invalid

Example of direct initialization:

model = NBeats(
    stack_types=["trend", "seasonality"],
    num_blocks=[3, 3],
    num_block_layers=[3, 3],
    widths=[32, 512],
    sharing=[True, True],
    expansion_coefficient_lengths=[3, 7],
    prediction_length=24,
    context_length=72,
)

Checklist

• Linked issues (if existing)
• Amended changelog for large changes (and added myself there as contributor)
• Added/modified tests
• Used pre-commit hooks when committing to ensure that code is compliant with hooks. Install hooks with pre-commit install.
  To run hooks independent of commit, execute pre-commit run --all-files

Make sure to have fun coding!

[codecov]

Codecov Report

Attention: Patch coverage is 90.00000% with 1 line in your changes missing coverage. Please review.

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Files with missing lines	Patch %	Lines
pytorch_forecasting/models/nbeats/_nbeats.py	90.00%	1 Missing ⚠️

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[fkiraly]

Thanks!

Quick question, why are we changing the old test? Does this mean existing code breaks?

We should avoid to break working code of users.

[MeherBhaskar]

Hi @fkiraly,
The change to update the widths from [4, 4, 4] to [4, 4] was made to match the number of stacks used by the N-BEATS model (["trend", "seasonality"])

Passing a list of length 3 was causing the following error:

ValueError: Length of widths (3) must match length of stack_types (2)

Let me know if you'd like to handle this differently!

[fkiraly]

Can you explain why the [4, 4, 4] was previously passing them in the tests? If it worked previously - and seems to have been standard expectation, an dnow we change it, we might break someone's code.