Pytorch-forecasting

Added

- Allow lists for multiple losses and normalizers (405)
- Warn if normalization is with scale `< 1e-7` (429)
- Allow usage of distribution losses in all settings (434)

Fixed

- Fix issue when predicting and data is on different devices (402)
- Fix non-iterable output (404)
- Fix problem with moving data to CPU for multiple targets (434)

Contributors

- jdb78
- domplexity

Added

- Adding a filter functionality to the timeseries datasset (329)
- Add simple models such as LSTM, GRU and a MLP on the decoder (380)
- Allow usage of any torch optimizer such as SGD (380)

Fixed

- Moving predictions to CPU to avoid running out of memory (329)
- Correct determination of `output_size` for multi-target forecasting with the TemporalFusionTransformer (328)
- Tqdm autonotebook fix to work outside of Jupyter (338)
- Fix issue with yaml serialization for TensorboardLogger (379)

Contributors

- jdb78
- JakeForsey
- vakker

Added

- Make tuning trainer kwargs overwritable (300)
- Allow adding categories to NaNEncoder (303)

Fixed

- Underlying data is copied if modified. Original data is not modified inplace (263)
- Allow plotting of interpretation on passed figure for NBEATS (280)
- Fix memory leak for plotting and logging interpretation (311)
- Correct shape of `predict()` method output for multi-targets (268)
- Remove cloudpickle to allow GPU trained models to be loaded on CPU devices from checkpoints (314)

Contributors

- jdb78
- kigawas
- snumumrik

- Added missing output transformation which was switched off by default (260)

Added

- Add "Release Notes" section to docs (237)
- Enable usage of lag variables for any model (252)

Changed

- Require PyTorch>=1.7 (245)

Fixed

- Fix issue for multi-target forecasting when decoder length varies in single batch (249)
- Enable longer subsequences for min_prediction_idx that were previously wrongfully excluded (250)

Contributors

- jdb78

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Added

- Adding support for multiple targets in the TimeSeriesDataSet (199) and amended tutorials.
- Temporal fusion transformer and DeepAR with support for multiple tagets (199)
- Check for non-finite values in TimeSeriesDataSet and better validate scaler argument (220)
- LSTM and GRU implementations that can handle zero-length sequences (235)
- Helpers for implementing auto-regressive models (236)

Changed

- TimeSeriesDataSet's `y` of the dataloader is a tuple of (target(s), weight) - potentially breaking for model or metrics implementation
Most implementations will not be affected as hooks in BaseModel and MultiHorizonMetric were modified. (199)

Fixed

- Fixed autocorrelation for pytorch 1.7 (220)
- Ensure reproducibility by replacing python `set()` with `dict.fromkeys()` (mostly TimeSeriesDataSet) (221)
- Ensures BetaDistributionLoss does not lead to infinite loss if actuals are 0 or 1 (233)
- Fix for GroupNormalizer if scaling by group (223)
- Fix for TimeSeriesDataSet when using `min_prediction_idx` (226)

Contributors

- jdb78
- JustinNeumann
- reumar
- rustyconover

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