added function for ensembling results

tsEnsemble.m

@@ -0,0 +1,10 @@
+function [nonStationaryEvaParamsEns, stationaryTransformDataEns]  = tsEnsemble( nonStatEvaParamsArray, stationaryTransformDataArray )
+  % calls tsEnsembleEvaParams and tsEnsembleStatTransfData.
+  % ASSUMING THAT ALL THE SERIES HAVE THE SAME LENGTH 
+  % AND UNIFORM TIME STAMPS
+
+  nonStationaryEvaParamsEns = tsEnsembleEvaParams(nonStatEvaParamsArray);
+  stationaryTransformDataEns = tsEnsembleStatTransfData(stationaryTransformDataArray);
+
+end
+

tsEnsembleEvaParams.m

@@ -0,0 +1,52 @@
+function nonStatEvaParamsEnsemble = tsEnsembleEvaParams( nonStatEvaParamsArray )
+  % From a cell array of non nonStatEvaParams computes the average
+  % nonStatEvaParamsArray is a cell array of nonStatEvaParams, the object
+  % type returned by tsEvaNonStationary
+  % ASSUMING THAT ALL THE SERIES HAVE THE SAME LENGTH
+  % AND UNIFORM TIME STAMPS
+
+  n = length(nonStatEvaParamsArray);
+  for ii = 1:n
+    nsep = nonStatEvaParamsArray{ii};
+    if ii == 1
+      ensep(1).method = 'GEVstat';
+      ensep(1).parameters = nsep(1).parameters;
+      ensep(1).paramErr = nsep(1).paramErr;
+      ensep(2).method = 'GPDstat';
+      ensep(2).parameters = nsep(2).parameters;
+      ensep(2).paramErr = nsep(2).paramErr;
+    else
+      ensep(1).parameters.epsilon = ensep(1).parameters.epsilon + nsep(1).parameters.epsilon;
+      ensep(1).parameters.sigma = ensep(1).parameters.sigma + nsep(1).parameters.sigma;
+      ensep(1).parameters.mu = ensep(1).parameters.mu + nsep(1).parameters.mu;
+      ensep(1).paramErr.epsilonErr = ensep(1).paramErr.epsilonErr + nsep(1).paramErr.epsilonErr;
+      ensep(1).paramErr.sigmaErr = ensep(1).paramErr.sigmaErr + nsep(1).paramErr.sigmaErr;
+      ensep(1).paramErr.muErr = ensep(1).paramErr.muErr + nsep(1).paramErr.muErr;
+
+      ensep(2).parameters.epsilon = ensep(2).parameters.epsilon + nsep(2).parameters.epsilon;
+      ensep(2).parameters.sigma = ensep(2).parameters.sigma + nsep(2).parameters.sigma;
+      ensep(2).parameters.threshold = ensep(2).parameters.threshold + nsep(2).parameters.threshold;
+      ensep(2).parameters.percentile = ensep(2).parameters.percentile + nsep(2).parameters.percentile;
+      ensep(2).paramErr.epsilonErr = ensep(2).paramErr.epsilonErr + nsep(2).paramErr.epsilonErr;
+      ensep(2).paramErr.sigmaErr = ensep(2).paramErr.sigmaErr + nsep(2).paramErr.sigmaErr;
+      ensep(2).paramErr.thresholdErr = ensep(2).paramErr.thresholdErr + nsep(2).paramErr.thresholdErr;
+    end
+  end
+
+  ensep(1).parameters.epsilon = ensep(1).parameters.epsilon/n;
+  ensep(1).parameters.sigma = ensep(1).parameters.sigma/n;
+  ensep(1).parameters.mu = ensep(1).parameters.mu/n;
+  ensep(1).paramErr.epsilonErr = ensep(1).paramErr.epsilonErr/n;
+  ensep(1).paramErr.sigmaErr = ensep(1).paramErr.sigmaErr/n;
+  ensep(1).paramErr.muErr = ensep(1).paramErr.muErr/n;
+  ensep(2).parameters.epsilon = ensep(2).parameters.epsilon/n;
+  ensep(2).parameters.sigma = ensep(2).parameters.sigma/n;
+  ensep(2).parameters.threshold = ensep(2).parameters.threshold/n;
+  ensep(2).parameters.percentile = ensep(2).parameters.percentile/n;
+  ensep(2).paramErr.epsilonErr = ensep(2).paramErr.epsilonErr/n;
+  ensep(2).paramErr.sigmaErr = ensep(2).paramErr.sigmaErr/n;
+  ensep(2).paramErr.thresholdErr = ensep(2).paramErr.thresholdErr/n;
+
+  nonStatEvaParamsEnsemble = ensep;
+end
+

tsEnsembleStatTransfData.m

@@ -0,0 +1,40 @@
+function [ stationaryTransformDataEnsemble ] = tsEnsembleStatTransfData( stationaryTransformDataArray )
+  % From a cell array of non stationaryTransformData computes the average
+  % stationaryTransformDataArray is a cell array of stationaryTransformData, 
+  % the transformation object type returned by tsEvaNonStationary
+  % ASSUMING THAT ALL THE SERIES HAVE THE SAME LENGTH 
+  % AND UNIFORM TIME STAMPS
+
+  n = length(stationaryTransformDataArray);
+  for ii=1:n
+    td = stationaryTransformDataArray{ii};
+    if ii == 1
+      etd.timeStamps = td.timeStamps;
+
+      etd.trendSeries = td.trendSeries;
+      etd.trendSeriesNonSeasonal = td.trendSeriesNonSeasonal;
+      etd.trendError = td.trendError;
+      etd.stdDevSeries = td.stdDevSeries;
+      etd.stdDevSeriesNonSeasonal = td.stdDevSeriesNonSeasonal;
+      etd.stdDevError = td.stdDevError;
+    else
+      etd.trendSeries = etd.trendSeries + td.trendSeries;
+      etd.trendSeriesNonSeasonal = etd.trendSeriesNonSeasonal + td.trendSeriesNonSeasonal;
+      etd.trendError = etd.trendError + td.trendError;
+      etd.stdDevSeries = etd.stdDevSeries + td.stdDevSeries;
+      etd.stdDevSeriesNonSeasonal = etd.stdDevSeriesNonSeasonal + td.stdDevSeriesNonSeasonal;
+      etd.stdDevError = etd.stdDevError + td.stdDevError;
+    end
+  end
+
+  etd.trendSeries = etd.trendSeries/n;
+  etd.trendSeriesNonSeasonal = etd.trendSeriesNonSeasonal/n;
+  etd.trendError = etd.trendError/n;
+  etd.stdDevSeries = etd.stdDevSeries/n;
+  etd.stdDevSeriesNonSeasonal = etd.stdDevSeriesNonSeasonal/n;
+  etd.stdDevError = etd.stdDevError/n;
+
+  stationaryTransformDataEnsemble = etd;
+
+end
+