https://github.com/sktime/pytorch-forecasting - v1.4.0

What's Changed

Feature and maintenance update.

See full changelog

New Contributors

• @gbilleyPeco made their first contribution in https://github.com/sktime/pytorch-forecasting/pull/1750
• @pietsjoh made their first contribution in https://github.com/sktime/pytorch-forecasting/pull/1399
• @MartinoMensio made their first contribution in https://github.com/sktime/pytorch-forecasting/pull/1579
• @phoeenniixx made their first contribution in https://github.com/sktime/pytorch-forecasting/pull/1811
• @cngmid made their first contribution in https://github.com/sktime/pytorch-forecasting/pull/1827
• @Marcrb2 made their first contribution in https://github.com/sktime/pytorch-forecasting/pull/1518
• @jobs-git made their first contribution in https://github.com/sktime/pytorch-forecasting/pull/1864

All Contributors

@agobbifbk, @Borda, @cngmid, @fkiraly, @fnhirwa, @gbilleyPeco, @jobs-git, @Marcrb2, @MartinoMensio, @phoeenniixx, @pietsjoh, @PranavBhatP

Full Changelog: https://github.com/sktime/pytorch-forecasting/compare/v1.3.0...v1.4.0

- Python
Published by fkiraly 9 months ago

https://github.com/sktime/pytorch-forecasting - v1.3.0

What's Changed

Feature and maintenance update.

• python 3.13 support
• tide model
• bugfixes for TFT

New Contributors

• @xiaokongkong made their first contribution in https://github.com/sktime/pytorch-forecasting/pull/1719
• @madprogramer made their first contribution in https://github.com/sktime/pytorch-forecasting/pull/1720
• @julian-fong made their first contribution in https://github.com/sktime/pytorch-forecasting/pull/1705
• @Sohaib-Ahmed21 made their first contribution in https://github.com/sktime/pytorch-forecasting/pull/1734
• @d-schmitt made their first contribution in https://github.com/sktime/pytorch-forecasting/pull/1580
• @Luke-Chesley made their first contribution in https://github.com/sktime/pytorch-forecasting/pull/1516
• @PranavBhatP made their first contribution in https://github.com/sktime/pytorch-forecasting/pull/1762

All Contributors

@d-schmitt, @fkiraly, @fnhirwa, @julian-fong, @Luke-Chesley, @madprogramer, @PranavBhatP, @Sohaib-Ahmed21, @xiaokongkong, @XinyuWuu

Full Changelog: https://github.com/sktime/pytorch-forecasting/compare/v1.2.0...v1.3.0

- Python
Published by fkiraly about 1 year ago

https://github.com/sktime/pytorch-forecasting - v1.2.0

What's Changed

Maintenance update, minor feature additions and bugfixes.

• support for numpy 2.X
• end of life for python 3.8
• fixed documentation build
• bugfixes

New Contributors

• @ewth made their first contribution in https://github.com/sktime/pytorch-forecasting/pull/1696
• @airookie17 made their first contribution in https://github.com/sktime/pytorch-forecasting/pull/1692
• @benHeid made their first contribution in https://github.com/sktime/pytorch-forecasting/pull/1704
• @eugenio-mercuriali made their first contribution in https://github.com/sktime/pytorch-forecasting/pull/1699

All Contributors

@airookie17, @benHeid, @eugenio-mercuriali, @ewth, @fkiraly, @fnhirwa, @XinyuWuu, @yarnabrina

Full Changelog: https://github.com/sktime/pytorch-forecasting/compare/v1.1.1...v1.2.0

- Python
Published by fkiraly over 1 year ago

https://github.com/sktime/pytorch-forecasting - v1.1.1

What's Changed

Hotfix release to correct typo in package name in pyproject.toml, to correct pytorch-forecasting PEP 440 identifier.

Otherwise identical with 1.1.0

Full Changelog: https://github.com/jdb78/pytorch-forecasting/compare/v1.1.0...v1.1.0

- Python
Published by fkiraly over 1 year ago

https://github.com/sktime/pytorch-forecasting - v1.1.0

What's Changed

Maintenance update widening compatibility ranges and consolidating dependencies:

• support for python 3.11 and 3.12, added CI testing
• support for MacOS, added CI testing
• core dependencies have been minimized to numpy, torch, lightning, scipy, pandas, and scikit-learn.
• soft dependencies are available in soft dependency sets: all_extras for all soft dependencies, and tuning for optuna based optimization.

Dependency changes

• the following are no longer core dependencies and have been changed to optional dependencies : optuna, statsmodels, pytorch-optimize, matplotlib. Environments relying on functionality requiring these dependencies need to be updated to install these explicitly.
• optuna bounds have been updated to optuna >=3.1.0,<4.0.0
• optuna-integrate is now an additional soft dependency, in case of optuna >=3.3.0

Deprecations and removals

• from 1.2.0, the default optimizer will be changed from "ranger" to "adam" to avoid non-torch dependencies in defaults. pytorch-optimize optimizers can still be used. Users should set the optimizer explicitly to continue using "ranger".
• from 1.1.0, the loggers do not log figures if soft dependency matplotlib is not present, but will raise no exceptions in this case. To log figures, ensure that matplotlib is installed.

All Contributors

@andre-marcos-perez, @avirsaha, @bendavidsteel, @benheid, @bohdan-safoniuk, @Borda, @CahidArda, @fkiraly, @fnhirwa, @germanKoch, @jacktang, @jdb78, @jurgispods, @maartensukel, @MBelniak, @orangehe, @pavelzw, @sfalkena, @tmct, @XinyuWuu, @yarnabrina,

New Contributors

• @jurgispods made their first contribution in https://github.com/jdb78/pytorch-forecasting/pull/1366
• @jacktang made their first contribution in https://github.com/jdb78/pytorch-forecasting/pull/1353
• @andre-marcos-perez made their first contribution in https://github.com/jdb78/pytorch-forecasting/pull/1346
• @tmct made their first contribution in https://github.com/jdb78/pytorch-forecasting/pull/1340
• @bohdan-safoniuk made their first contribution in https://github.com/jdb78/pytorch-forecasting/pull/1318
• @MBelniak made their first contribution in https://github.com/jdb78/pytorch-forecasting/pull/1230
• @CahidArda made their first contribution in https://github.com/jdb78/pytorch-forecasting/pull/1175
• @bendavidsteel made their first contribution in https://github.com/jdb78/pytorch-forecasting/pull/1359
• @Borda made their first contribution in https://github.com/jdb78/pytorch-forecasting/pull/1498
• @fkiraly made their first contribution in https://github.com/jdb78/pytorch-forecasting/pull/1598
• @XinyuWuu made their first contribution in https://github.com/jdb78/pytorch-forecasting/pull/1599
• @pavelzw made their first contribution in https://github.com/jdb78/pytorch-forecasting/pull/1407
• @yarnabrina made their first contribution in https://github.com/jdb78/pytorch-forecasting/pull/1630
• @fnhirwa made their first contribution in https://github.com/jdb78/pytorch-forecasting/pull/1646
• @avirsaha made their first contribution in https://github.com/jdb78/pytorch-forecasting/pull/1649

Full Changelog: https://github.com/jdb78/pytorch-forecasting/compare/v1.0.0...v1.1.0

- Python
Published by fkiraly over 1 year ago

https://github.com/sktime/pytorch-forecasting - Update to pytorch 2.0

Breaking Changes

• Upgraded to pytorch 2.0 and lightning 2.0. This brings a couple of changes, such as configuration of trainers. See the lightning upgrade guide. For PyTorch Forecasting, this particularly means if you are developing own models, the class method epoch_end has been renamed to on_epoch_end and replacing model.summarize() with ModelSummary(model, max_depth=-1) and Tuner(trainer) is its own class, so trainer.tuner needs replacing. (#1280)
• Changed the predict() interface returning named tuple - see tutorials.

Changes

• The predict method is now using the lightning predict functionality and allows writing results to disk (#1280).

Fixed

• Fixed robust scaler when quantiles are 0.0, and 1.0, i.e. minimum and maximum (#1142)

- Python
Published by jdb78 almost 3 years ago

https://github.com/sktime/pytorch-forecasting - Poetry update

Fixed

• Removed pandoc from dependencies as issue with poetry install (#1126)
• Added metric attributes for torchmetric resulting in better multi-GPU performance (#1126)

Added

• "robust" encoder method can be customized by setting "center", "lower" and "upper" quantiles (#1126)

- Python
Published by jdb78 over 3 years ago

https://github.com/sktime/pytorch-forecasting - Multivariate networks

Added

• DeepVar network (#923)
• Enable quantile loss for N-HiTS (#926)
• MQF2 loss (multivariate quantile loss) (#949)
• Non-causal attention for TFT (#949)
• Tweedie loss (#949)
• ImplicitQuantileNetworkDistributionLoss (#995)

Fixed

• Fix learning scale schedule (#912)
• Fix TFT list/tuple issue at interpretation (#924)
• Allowed encoder length down to zero for EncoderNormalizer if transformation is not needed (#949)
• Fix Aggregation and CompositeMetric resets (#949)

Changed

• Dropping Python 3.6 suppport, adding 3.10 support (#479)
• Refactored dataloader sampling - moved samplers to pytorch_forecasting.data.samplers module (#479)
• Changed transformation format for Encoders to dict from tuple (#949)

Contributors

• jdb78

- Python
Published by jdb78 almost 4 years ago

https://github.com/sktime/pytorch-forecasting - Bugfixes

Fixed

• Fix with creating tensors on correct devices (#908)
• Fix with MultiLoss when calculating gradient (#908)

Contributors

• jdb78

- Python
Published by jdb78 almost 4 years ago

https://github.com/sktime/pytorch-forecasting - Adding N-HiTS network (N-BEATS successor)

Added

• Added new N-HiTS network that has consistently beaten N-BEATS (#890)
• Allow using torchmetrics as loss metrics (#776)
• Enable fitting EncoderNormalizer() with limited data history using max_length argument (#782)
• More flexible MultiEmbedding() with convenience output_size and input_size properties (#829)
• Fix concatentation of attention (#902)

Fixed

• Fix pip install via github (#798)

Contributors

• jdb78
• christy
• lukemerrick
• Seon82

- Python
Published by jdb78 almost 4 years ago

https://github.com/sktime/pytorch-forecasting - Maintenance Release

Added

• Added support for running pytorch_lightning.trainer.test (#759)

Fixed

• Fix inattention mutation to x_cont (#732).
• Compatability with pytorch-lightning 1.5 (#758)

Contributors

• eavae
• danielgafni
• jdb78

- Python
Published by jdb78 over 4 years ago

https://github.com/sktime/pytorch-forecasting - Maintenance Release (26/09/2021)

Added

• Use target name instead of target number for logging metrics (#588)
• Optimizer can be initialized by passing string, class or function (#602)
• Add support for multiple outputs in Baseline model (#603)
• Added Optuna pruner as optional parameter in TemporalFusionTransformer.optimize_hyperparameters (#619)
• Dropping support for Python 3.6 and starting support for Python 3.9 (#639)

Fixed

• Initialization of TemporalFusionTransformer with multiple targets but loss for only one target (#550)
• Added missing transformation of prediction for MLP (#602)
• Fixed logging hyperparameters (#688)
• Ensure MultiNormalizer fit state is detected (#681)
• Fix infinite loop in TimeDistributedEmbeddingBag (#672)

Contributors

• jdb78
• TKlerx
• chefPony
• eavae
• L0Z1K

- Python
Published by jdb78 over 4 years ago

https://github.com/sktime/pytorch-forecasting - Simplified API

Breaking changes

• Removed dropout_categoricals parameter from TimeSeriesDataSet. Use categorical_encoders=dict(<variable_name>=NaNLabelEncoder(add_nan=True)) instead (#518)
• Rename parameter allow_missings for TimeSeriesDataSet to allow_missing_timesteps (#518)

• Transparent handling of transformations. Forward methods should now call two new methods (#518):

  • transform_output to explicitly rescale the network outputs into the de-normalized space
  • to_network_output to create a dict-like named tuple. This allows tracing the modules with PyTorch's JIT. Only prediction is still required which is the main network output.

Example:

python def forward(self, x): normalized_prediction = self.module(x) prediction = self.transform_output(prediction=normalized_prediction, target_scale=x["target_scale"]) return self.to_network_output(prediction=prediction)

Added

• Improved validation of input parameters of TimeSeriesDataSet (#518)

Fixed

• Fix quantile prediction for tensors on GPUs for distribution losses (#491)
• Fix hyperparameter update for RecurrentNetwork.from_dataset method (#497)

- Python
Published by jdb78 over 4 years ago

https://github.com/sktime/pytorch-forecasting - 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

- Python
Published by jdb78 almost 5 years ago

https://github.com/sktime/pytorch-forecasting - 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

- Python
Published by jdb78 almost 5 years ago

https://github.com/sktime/pytorch-forecasting - 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

- Python
Published by jdb78 about 5 years ago

https://github.com/sktime/pytorch-forecasting - Fix for output transformer

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

- Python
Published by jdb78 about 5 years ago

https://github.com/sktime/pytorch-forecasting - 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 minpredictionidx that were previously wrongfully excluded (#250)

Contributors

• jdb78

- Python
Published by jdb78 about 5 years ago

https://github.com/sktime/pytorch-forecasting - 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

- Python
Published by jdb78 about 5 years ago

https://github.com/sktime/pytorch-forecasting - 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)

- Python
Published by jdb78 about 5 years ago

https://github.com/sktime/pytorch-forecasting - 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

- Python
Published by jdb78 about 5 years ago

https://github.com/sktime/pytorch-forecasting - 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

- Python
Published by jdb78 over 5 years ago

https://github.com/sktime/pytorch-forecasting - 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

- Python
Published by jdb78 over 5 years ago

https://github.com/sktime/pytorch-forecasting - Bug fixes

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)

- Python
Published by jdb78 over 5 years ago

https://github.com/sktime/pytorch-forecasting - Fixes to interpretation and more control over hyperparameter verbosity

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)

- Python
Published by jdb78 over 5 years ago

https://github.com/sktime/pytorch-forecasting - PyTorch Lightning 1.0 compatibility

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

- Python
Published by jdb78 over 5 years ago

https://github.com/sktime/pytorch-forecasting - PyTorch Lightning 0.10 compatibility and classification

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 trainer.tuner.lr_find() instead of trainer.lr_find() in tutorials and examples
• Update poetry to 1.1.0

- Python
Published by jdb78 over 5 years ago

https://github.com/sktime/pytorch-forecasting - Various fixes models and data

Fixes

Model

• Removed attention to current datapoint in TFT decoder to generalise better over various sequence lengths
• Allow resuming optuna hyperparamter tuning study

Data

• Fixed inconsistent naming and calculation of encoder_lengthin TimeSeriesDataSet when added as feature

Contributors

• jdb78

- Python
Published by jdb78 over 5 years ago

https://github.com/sktime/pytorch-forecasting - Metrics, performance, and subsequence detection

Added

Models

• Backcast loss for N-BEATS network for better regularisation
• logging_metrics as explicit arguments to models

Metrics

• MASE (Mean absolute scaled error) metric for training and reporting
• Metrics can be composed, e.g. 0.3* metric1 + 0.7 * metric2
• Aggregation metric that is computed on mean prediction over all samples to reduce mean-bias

Data

• Increased speed of parsing data with missing datapoints. About 2s for 1M data points. If numba is installed, 0.2s for 1M data points
• Time-synchronize samples in batches: ensure that all samples in each batch have with same time index in decoder

Breaking changes

• Improved subsequence detection in TimeSeriesDataSet ensures that there exists a subsequence starting and ending on each point in time.
• Fix min_encoder_length = 0 being ignored and processed as min_encoder_length = max_encoder_length

Contributors

• jdb78
• dehoyosb

- Python
Published by jdb78 over 5 years ago

https://github.com/sktime/pytorch-forecasting - More tests and better docs

• More tests driving coverage to ~90%
• Performance tweaks for temporal fusion transformer
• Reformatting with sort
• Improve documentation - particularly expand on hyper parameter tuning

Fixes: * Fix PoissonLoss quantiles calculation * Fix N-Beats visualisations

- Python
Published by jdb78 over 5 years ago

https://github.com/sktime/pytorch-forecasting - More testing and interpretation features

Added

• Calculating partial dependency for a variable
• Improved documentation - in particular added FAQ section and improved tutorial
• Data for examples and tutorials can now be downloaded. Cloning the repo is not a requirement anymore
• Added Ranger Optimizer from pytorch_ranger package and fixed its warnings (part of preparations for conda package release)
• Use GPU for tests if available as part of preparation for GPU tests in CI

Changes

• BREAKING: Fix typo “adddecoderlength” to “addencoderlength” in TimeSeriesDataSet

Bugfixes

• Fixing plotting predictions vs actuals by slicing variables

- Python
Published by jdb78 over 5 years ago

https://github.com/sktime/pytorch-forecasting - Fix edge case in prediction logging

Fixes

Fix bug where predictions were not correctly logged in case of decoder_length == 1.

Additions

Add favicon to docs page

- Python
Published by jdb78 over 5 years ago

https://github.com/sktime/pytorch-forecasting - Make pip installable from master branch

Update build system requirements to be parsed correctly when installing with pip install https://github.com/jdb78/pytorch-forecasting/

- Python
Published by jdb78 over 5 years ago

https://github.com/sktime/pytorch-forecasting - Improving tests

• Add tests for MacOS
• Automatic releases
• Coverage reporting

- Python
Published by jdb78 over 5 years ago

https://github.com/sktime/pytorch-forecasting - Patch release

This release improves robustness of the code.

Fixing bug across code, in particularly

• Ensuring that code works on GPUs
• Adding tests for models, dataset and normalisers
• Test using GitHub Actions (tests on GPU are still missing)

Extend documentation by improving docstrings and adding two tutorials.

Improving default arguments for TimeSeriesDataSet to avoid surprises

- Python
Published by jdb78 over 5 years ago

https://github.com/sktime/pytorch-forecasting - Minor release

Minor release

Added

• Basic tests for data and model (mostly integration tests)
• Automatic target normalization
• Improved visualization and logging of temporal fusion transformer
• Model bugfixes and performance improvements for temporal fusion transformer

Modified

• Metrics are reduced to calculating loss. Target transformations are done by new target transformer

- Python
Published by jdb78 over 5 years ago