Generic distribution loss(es)

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

Simple models

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

Bugfix release

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

Fix for output transformer

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

Adding support for lag variables

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

Adding multi-target support

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.

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

Tutorial on how to implement a new architecture

Added

• Tutorial on how to implement a new architecture covering basic and advanced use cases (#188)
• Additional and improved documentation - particularly of implementation details (#188)

Changed (breaking for new model implementations)

• Moved multiple private methods to public methods (particularly logging) (#188)
• Moved get_mask method from BaseModel into utils module (#188)
• Instead of using label to communicate if model is training or validating, using self.training attribute (#188)
• Using sample((n,)) of pytorch distributions instead of deprecated sample_n(n) method (#188)

New API for transforming inputs and outputs with encoders

Added

• Beta distribution loss for probabilistic models such as DeepAR (#160)

Changed

• BREAKING: Simplifying how to apply transforms (such as logit or log) before and after applying encoder. Some transformations are included by default but a tuple of a forward and reverse transform function can be passed for arbitrary transformations. This requires to use a transformation keyword in target normalizers instead of, e.g. log_scale (#185)

Fixed

• Incorrect target position if len(static_reals) > 0 leading to leakage (#184)
• Fixing predicting completely unseen series (#172)

Contributors

• jdb78
• JakeForsey

Bugfixes and DeepAR improvements

Added

• Using GRU cells with DeepAR (#153)

Fixed

• GPU fix for variable sequence length (#169)
• Fix incorrect syntax for warning when removing series (#167)
• Fix issue when using unknown group ids in validation or test dataset (#172)
• Run non-failing CI on PRs from forks (#166, #156)

Docs

• Improved model selection guidance and explanations on how TimeSeriesDataSet works (#148)
• Clarify how to use with conda (#168)

Contributors

• jdb78
• JakeForsey

Adding DeepAR

Added

• DeepAR by Amazon (#115)
  • First autoregressive model in PyTorch Forecasting
  • Distribution loss: normal, negative binomial and log-normal distributions
  • Currently missing: handling lag variables and tutorial (planned for 0.6.1)
• Improved documentation on TimeSeriesDataSet and how to implement a new network (#145)

Changed

• Internals of encoders and how they store center and scale (#115)

Fixed

• Update to PyTorch 1.7 and PyTorch Lightning 1.0.5 which came with breaking changes for CUDA handling and with optimizers (PyTorch Forecasting Ranger version) (#143, #137, #115)

Contributors

• jdb78
• JakeForesey