Pytorch-forecasting

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

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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

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Fixes

- Fix issue where hyperparameter verbosity controlled only part of output (118)
- Fix occasional error when `.get_parameters()` from `TimeSeriesDataSet` failed (117)
- Remove redundant double pass through LSTM for temporal fusion transformer (125)
- Prevent installation of pytorch-lightning 1.0.4 as it breaks the code (127)
- Prevent modification of model defaults in-place (112)

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Added

- Hyperparameter tuning with optuna to tutorial
- Control over verbosity of hyper parameter tuning

Fixes

- Interpretation error when different batches had different maximum decoder lengths
- Fix some typos (no changes to user API)

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This release has only one purpose: Allow usage of PyTorch Lightning 1.0 - all tests have passed.

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Added

- Additional checks for `TimeSeriesDataSet` inputs - now flagging if series are lost due to high `min_encoder_length` and ensure parameters are integers
- Enable classification - simply change the target in the `TimeSeriesDataSet` to a non-float variable, use the `CrossEntropy` metric to optimize and output as many classes as you want to predict

Changed

- Ensured PyTorch Lightning 0.10 compatibility
- Using `LearningRateMonitor` instead of `LearningRateLogger`
- Use `EarlyStopping` callback in trainer `callbacks` instead of `early_stopping` argument
- Update metric system `update()` and `compute()` methods
- Use `Tuner(trainer).lr_find()` instead of `trainer.lr_find()` in tutorials and examples
- Update poetry to 1.1.0

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