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Release Notes

v1.1.1

Hotfix for accidental package name change in pyproject.toml.

The package name is now corrected to pytorch-forecasting.

v1.1.0

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 instlal 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 tha matplotlib is installed.

v1.0.0 Update to pytorch 2.0 (10/04/2023)

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)

v0.10.3 Poetry update (07/09/2022)

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)

v0.10.2 Multivariate networks (23/05/2022)

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

v0.10.1 Bugfixes (24/03/2022)

Fixed

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

Contributors

  • jdb78

v0.10.0 Adding N-HiTS network (N-BEATS successor) (23/03/2022)

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

v0.9.2 Maintenance Release (30/11/2021)

Added

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

Fixed

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

Contributors

  • eavae
  • danielgafni
  • jdb78

v0.9.1 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

v0.9.0 Simplified API (04/06/2021)

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:

    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)

Fixed

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

Added

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

v0.8.5 Generic distribution loss(es) (27/04/2021)

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

v0.8.4 Simple models (07/03/2021)

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

v0.8.3 Bugfix release (31/01/2021)

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

v0.8.2 Fix for output transformer (12/01/2021)

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

v0.8.1 Adding support for lag variables (10/01/2021)

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

v0.8.0 Adding multi-target support (03/01/2021)

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

v0.7.1 Tutorial on how to implement a new architecture (07/12/2020)

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)

v0.7.0 New API for transforming inputs and outputs with encoders (03/12/2020)

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

v0.6.1 Bugfixes and DeepAR improvements (24/11/2020)

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

v0.6.0 Adding DeepAR (10/11/2020)

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

v0.5.3 Bug fixes (31/10/2020)

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)

v0.5.2 Fixes to interpretation and more control over hyperparameter verbosity (18/10/2020)

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)

v0.5.1 PyTorch Lightning 1.0 compatibility (14/10/2020)

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

v0.5.0 PyTorch Lightning 0.10 compatibility and classification (12/10/2020)

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

v0.4.1 Various fixes models and data (01/10/2020)

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

v0.4.0 Metrics, performance, and subsequence detection (28/09/2020)

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

v0.3.1 More tests and better docs (13/09/2020)

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

Fixed

  • Fix PoissonLoss quantiles calculation
  • Fix N-Beats visualisations

v0.3.0 More testing and interpretation features (02/09/2020)

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 "add_decoder_length" to "add_encoder_length" in TimeSeriesDataSet

Bugfixes

  • Fixing plotting predictions vs actuals by slicing variables

v0.2.4 Fix edge case in prediction logging (26/08/2020)

Fixed

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

Added

  • Add favicon to docs page

v0.2.3 Make pip installable from master branch (23/08/2020)

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

v0.2.2 Improving tests (23/08/2020)

  • Add tests for MacOS
  • Automatic releases
  • Coverage reporting

v0.2.1 Patch release (23/08/2020)

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

v0.2.0 Minor release (16/08/2020)

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