Feat/local and global models in EnsembleModel #1745
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…al and Global models
…single ts training/inference only. If provided, the covariates are passed only to the models supporting them (individually)
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…the same covariates, to make the behavior more transparent
dennisbader
requested changes
May 19, 2023
Co-authored-by: Dennis Bader <dennis.bader@gmx.ch>
… added tests for covariates support in ensemble models
…nit8co/darts into feat/local-and-global-ensemble
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dennisbader
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May 23, 2023
Co-authored-by: Dennis Bader <dennis.bader@gmx.ch>
…model, support_*_covariates for RegressionModels rely on the lags attribute
dennisbader
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May 26, 2023
dennisbader
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May 26, 2023
alexcolpitts96
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May 31, 2023
* feat: remove restriction in EnsembleModel, models can be a mix of Local and Global models * feat: EnsembleModel accepts a mixture of local and global models for single ts training/inference only. If provided, the covariates are passed only to the models supporting them (individually) * feat: updated unittests * doc: fix typo in docstring, SeasonalityMode must be imported from darts.utils.utils * doc: updated changelog * feat: logger info when all the models in the ensemble do not support the same covariates, to make the behavior more transparent * fix: typo, using parenthesis to call proterty method * Apply suggestions from code review Co-authored-by: Dennis Bader <dennis.bader@gmx.ch> * fix: made the covariates handling in ensemble model more transparent, added tests for covariates support in ensemble models * Update CHANGELOG.md * Update CHANGELOG.md * Update CHANGELOG.md * Apply suggestions from code review Co-authored-by: Dennis Bader <dennis.bader@gmx.ch> * fix: addressed reviewer comments, added show_warning arg to ensemble_model, support_*_covariates for RegressionModels rely on the lags attribute * fix typo * fix: improve warning synthax --------- Co-authored-by: Dennis Bader <dennis.bader@gmx.ch>
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Fixes #1492, fixes #737, fixes #1038.
Summary
EnsembleModel.modelsnow accept a mix ofLocalandGlobalForecastingModel. If the ensemble contains at least one local model, it only supports singleTimeSeriestraining/inference.If past/future covariates are provided, they will be passed only the models supporting them (not enforcing same covariates for all the models). I don't know if this is desirable or if all the models should support the exact same covariates in order to belong to the same
EnsembleModel?Other Information
I wonder if we should also allow pre-trained
GlobalForecastingModelinEnsembleModelwith a flag to bypass the exception currently raised (and make sure the user is aware of the implications). This could be quite powerful but would open the door for data leakage that could be difficult to detect:RegressionEnsembleModel) : undetectable