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Changelog

We do our best to avoid the introduction of breaking changes, but cannot always guarantee backwards compatibility. Changes that may break code which uses a previous release of Darts are marked with a "🔴".

Unreleased

Full Changelog

0.24.0 (2023-04-12)

For users of the library:

Improved

  • General model improvements:
    • New baseline forecasting model NaiveMovingAverage. #1557 by Janek Fidor.
    • New models StatsForecastAutoCES, and StatsForecastAutoTheta from Nixtla's statsforecasts library as local forecasting models without covariates support. AutoTheta supports probabilistic forecasts. #1476 by Boyd Biersteker.
    • Added support for future covariates, and probabilistic forecasts to StatsForecastAutoETS. #1476 by Boyd Biersteker.
    • Added support for logistic growth to Prophet with parameters growth, cap, floor. #1419 by David Kleindienst.
    • Improved the model string / object representation style similar to scikit-learn models. #1590 by Janek Fidor.
    • 🔴 Renamed MovingAverage to MovingAverageFilter to avoid confusion with new NaiveMovingAverage model. #1557 by Janek Fidor.
  • Improvements to RegressionModel:
    • Optimized lagged data creation for fit/predict sets achieving a drastic speed-up. #1399 by Matt Bilton.
    • Added support for categorical past/future/static covariates to LightGBMModel with model creation parameters categorical_*_covariates. #1585 by Rijk van der Meulen.
    • Added lagged feature names for better interpretability; accessible with model property lagged_feature_names. #1679 by Antoine Madrona.
    • 🔴 New use_static_covariates option for all models: When True (default), models use static covariates if available at fitting time and enforce identical static covariate shapes across all target series used for training or prediction; when False, models ignore static covariates. #1700 by Dennis Bader.
  • Improvements to TorchForecastingModel:
    • New methods load_weights() and load_weights_from_checkpoint() for loading only the weights from a manually saved model or checkpoint. This allows to fine-tune the pre-trained models with different optimizers or learning rate schedulers. #1501 by Antoine Madrona.
    • New method lr_find() that helps to find a good initial learning rate for your forecasting problem. #1609 by Levente Szabados and Dennis Bader.
    • Improved the user guide and added new sections about saving/loading (checkpoints, manual save/load, loading weights only), and callbacks. #1661 by Antoine Madrona.
    • 🔴 Replaced ":" in save file names with "_" to avoid issues on some operating systems. For loading models saved on earlier Darts versions, try to rename the file names by replacing ":" with "_". #1501 by Antoine Madrona.
    • 🔴 New use_static_covariates option for TFTModel, DLinearModel and NLinearModel: When True (default), models use static covariates if available at fitting time and enforce identical static covariate shapes across all target series used for training or prediction; when False, models ignore static covariates. #1700 by Dennis Bader.
  • Improvements to TimeSeries:
    • Added support for integer indexed input to from_* factory methods, if index can be converted to a pandas.RangeIndex. #1527 by Dennis Bader.
    • Added support for integer indexed input with step sizes (freq) other than 1. #1527 by Dennis Bader.
    • Optimized time series creation with fill_missing_dates=True achieving a drastic speed-up . #1527 by Dennis Bader.
    • from_group_dataframe() now warns the user if there is suspicion of a "bad" time index (monotonically increasing). #1628 by Dennis Bader.
  • Added a parameter to give a custom function name to the transformed output of WindowTransformer; improved the explanation of the window parameter. #1676 and #1666 by Jing Qiang Goh.
  • Added historical_forecasts parameter to backtest() that allows to use precomputed historical forecasts from historical_forecasts(). #1597 by Janek Fidor.
  • Added feature values and SHAP object to ShapExplainabilityResult, giving easy user access to all SHAP-specific explainability results. #1545 by Rijk van der Meulen.
  • New quantile_loss() (pinball loss) metric for probabilistic forecasts. #1559 by Janek Fidor.

Fixed

  • Fixed an issue in BottomUp/TopDownReconciliator where the order of the series components was not taken into account. #1592 by David Kleindienst.
  • Fixed an issue with DLinearModel not supporting even numbered kernel_size. #1695 by Antoine Madrona.
  • Fixed an issue with RegressionEnsembleModel not using future covariates during training. #1660 by Rajesh Balakrishnan.
  • Fixed an issue where NaiveEnsembleModel prediction did not transfer the series' component name. #1602 by David Kleindienst.
  • Fixed an issue in TorchForecastingModel that prevented from using multi GPU training. #1509 by Levente Szabados.
  • Fixed a bug when saving a FFT model with trend=None. #1594 by Antoine Madrona.
  • Fixed some issues with PyTorch-Lightning version 2.0.0. #1651 by Dennis Bader.
  • Fixed a bug in QuantileDetector which raised an error when low and high quantiles had identical values. #1553 by Julien Adda.
  • Fixed an issue preventing TimeSeries from being empty. #1359 by Antoine Madrona.
  • Fixed an issue when using backtest() on multiple series. #1517 by Julien Herzen.
  • General fixes to historical_forecasts()
    • Fixed issue where retrain functions were not handled properly; Improved handling of start, and train_length parameters; better interpretability with warnings and improved error messages (warnings can be turned of with show_warnings=False). By #1675 by Antoine Madrona and Dennis Bader.
    • Fixed an issue for several models (mainly ensemble and local models) where automatic start did not respect the minimum required training lengths. #1616 by Janek Fidor and Dennis Bader.
    • Fixed an issue when using a RegressionModel with future covariates lags only. #1685 by Maxime Dumonal.

For developers of the library:

Improvements

  • Option to skip slow tests locally with pytest . --no-cov -m "not slow". #1625 by Blazej Nowicki.
  • Major refactor of data transformers which simplifies implementation of new transformers. #1409 by Matt Bilton.

0.23.1 (2023-01-12)

Patch release

Fixed

  • Fix an issue in TimeSeries which made it incompatible with Python 3.7. #1449 by Dennis Bader.
  • Fix an issue with static covariates when series have variable lengths with RegressionModels. #1469 by Eliane Maalouf.
  • Fix an issue with PyTorch Lightning trainer handling. #1459 by Dennis Bader.
  • Fix an issue with historical_forecasts() retraining PyTorch models iteratively instead of from scratch. #1465 by Dennis Bader.
  • Fix an issue with historical_forecasts() not working in some cases when future_covariates are provided and start is not specified. #1481 by Maxime Dumonal.
  • Fix an issue with slice_n_points functions on integer indexes. #1482 by Julien Herzen.

0.23.0 (2022-12-23)

For users of the library:

Improved

  • 🚀🚀🚀 Brand new Darts module dedicated to anomaly detection on time series: darts.ad. More info on the API doc page: https://unit8co.github.io/darts/generated_api/darts.ad.html. #1256 by Julien Adda and Julien Herzen.
  • New forecasting models: DLinearModel and NLinearModel as proposed in this paper. #1139 by Julien Herzen and Greg DeVos.
  • New forecasting model: XGBModel implementing XGBoost. #1405 by Julien Herzen.
  • New multi_models option for all RegressionModels: when set to False, uses only a single underlying estimator for multi-step forecasting, which can drastically increase computational efficiency. #1291 by Eliane Maalouf.
  • All RegressionModels (incl. LightGBM, Catboost, XGBoost, Random Forest, ...) now support static covariates. #1412 by Eliane Maalouf.
  • historical_forecasts() and backtest() now work on multiple series, too. #1318 by Maxime Dumonal.
  • New window transformation capabilities: TimeSeries.window_transform() and a new WindowTransformer which allow to easily create window features. #1269 by Eliane Maalouf.
  • 🔴 Improvements to TorchForecastingModels: Load models directly to CPU that were trained on GPU. Save file size reduced. Improved PyTorch Lightning Trainer handling fixing several minor issues. Removed deprecated methods load_model and save_model #1371 by Dennis Bader.
  • Improvements to encoders: Added support for encoders to all models with covariate support through add_encoders at model creation. Encoders now generate the correct minimum required covariate time spans for all models. #1338 by Dennis Bader.
  • New datasets available in darts.datasets (ILINetDataset, ExchangeRateDataset, TrafficDataset, WeatherDataset) #1298 by Kamil Wierciak. #1291 by Eliane Maalouf.
  • New Diff transformer, which can difference and "undifference" series #1380 by Matt Bilton.
  • Improvements to KalmanForecaster: The model now accepts different TimeSeries for prediction than the ones used to fit the model. #1338 by Dennis Bader.
  • Backtest functions can now accept a list of metric functions #1333 by Antoine Madrona.
  • Extension of baseline models to work on multivariate series #1373 by Błażej Nowicki.
  • Improvement to TimeSeries.gaps() #1265 by Antoine Madrona.
  • Speedup of TimeSeries.quantile_timeseries() method #1351 by @tranquilitysmile.
  • Some dependencies which can be hard to install (LightGBM, Catboost, XGBoost, Prophet, Statsforecast) are not required anymore (if not installed the corresponding models will not be available) #1360 by Antoine Madrona.
  • Removed IPython as a dependency. #1331 by Erik Hasse
  • Allow the creation of empty TimeSeries #1359 by Antoine Madrona.

Fixed

0.22.0 (2022-10-04)

For users of the library:

Improved

  • New explainability feature. The class ShapExplainer in darts.explainability can provide Shap-values explanations of the importance of each lag and each dimension in producing each forecasting lag for RegressionModels. #909 by Maxime Dumonal.
  • New model: StatsForecastsETS. Similarly to StatsForecastsAutoARIMA, this model offers the ETS model from Nixtla's statsforecasts library as a local forecasting model supporting future covariates. #1171 by Julien Herzen.
  • Added support for past and future covariates to residuals() function. #1223 by Eliane Maalouf.
  • Added support for retraining model(s) every n iteration and on custom conditions in historical_forecasts method of ForecastingModels. #1139 by Francesco Bruzzesi.
  • Added support for beta-NLL in GaussianLikelihoods, as proposed in this paper. #1162 by Julien Herzen.
  • New LayerNorm alternatives, RMSNorm and LayerNormNoBias #1113 by Greg DeVos.
  • 🔴 Improvements to encoders: improve fitting behavior of encoders' transformers and solve a couple of issues. Remove support for absolute index encoding. #1257 by Dennis Bader.
  • Overwrite min_train_series_length for Catboost and LightGBM #1214 by Anne de Vries.
  • New example notebook showcasing and end-to-end example of hyperparameter optimization with Optuna #1242 by Julien Herzen.
  • New user guide section on hyperparameter optimization with Optuna and Ray Tune #1242 by Julien Herzen.
  • Documentation on model saving and loading. #1210 by Amadej Kocbek.
  • 🔴 torch_device_str has been removed from all torch models in favor of Pytorch Lightning's pl_trainer_kwargs method #1244 by Greg DeVos.

Fixed

0.21.0 (2022-08-12)

For users of the library:

Improved

  • New model: Catboost, incl quantile, poisson and gaussian likelihoods support. #1007, #1044 by Jonas Racine.
  • Extension of the add_encoders option to RegressionModels. It is now straightforward to add calendar based or custom past or future covariates to these models, similar to torch models. #1093 by Dennis Bader.
  • Introduction of StaticCovariatesTransformer, categorical static covariate support for TFTModel, example and user-guide updates on static covariates. #1081 by Dennis Bader.
  • ARIMA and VARIMA models now support being applied to a new series, different than the one used for training. #1036 by Samuele Giuliano Piazzetta.
  • All Darts forecasting models now have unified save() and load() methods. #1070 by Dustin Brunner.
  • Improvements in logging. #1034 by Dustin Brunner.
  • Re-integrating Prophet >= 1.1 in core dependencies (as it does not depend on PyStan anymore). #1054 by Julien Herzen.
  • Added a new AustralianTourismDataset. #1141 by Julien Herzen.
  • Added a new notebook demonstrating hierarchical reconciliation. #1147 by Julien Herzen.
  • Added drop_columns() method to TimeSeries. #1040 by @shaido987
  • Speedup static covariates when no casting is needed. #1053 by Julien Herzen.
  • Implemented the min_train_series_length method for the FourTheta and Theta models that overwrites the minimum default of 3 training samples by 2*seasonal_period when appropriate. #1101 by Rijk van der Meulen.
  • Make default formatting optional in plots. #1056 by Colin Delahunty
  • Introduce retrain option in residuals() method. #1066 by Julien Herzen.
  • Improved error messages. #1066 by Julien Herzen.
  • Small readability improvements to user guide. #1039, #1046 by Ryan Russell

Fixed

0.20.0 (2022-06-22)

For users of the library:

Improved

  • Added support for static covariates in TimeSeries class. #966 by Dennis Bader.
  • Added support for static covariates in TFT model. #966 by Dennis Bader.
  • Support for storing hierarchy of components in TimeSeries (in view of hierarchical reconciliation) #1012 by Julien Herzen.
  • New Reconciliation transformers for forecast reconciliation: bottom up, top down and MinT. #1012 by Julien Herzen.
  • Added support for Monte Carlo Dropout, as a way to capture model uncertainty with torch models at inference time. #1013 by Julien Herzen.
  • New datasets: ETT and Electricity. #617 by Greg DeVos
  • New dataset: Uber TLC. #1003 by Greg DeVos.
  • Model Improvements: Option for changing activation function for NHiTs and NBEATS. NBEATS support for dropout. NHiTs Support for AvgPooling1d. #955 by Greg DeVos.
  • Implemented "GLU Variants Improve Transformer" for transformer based models (transformer and TFT). #959 by Greg DeVos.
  • Added support for torch metrics during training and validation. #996 by Greg DeVos.
  • Better handling of logging #1010 by Dustin Brunner.
  • Better support for Python 3.10, and dropping prophet as a dependency (Prophet model still works if prophet package is installed separately) #1023 by Julien Herzen.
  • Option to avoid global matplotlib configuration changes. #924 by Mike Richman.
  • 🔴 HNiTSModel renamed to HNiTS #1000 by Greg DeVos.

Fixed

  • A bug with tail() and head() #942 by Julien Herzen.
  • An issue with arguments being reverted for the metric function of gridsearch and backtest #989 by Clara Grotehans.
  • An error checking whether fit() has been called in global models #944 by Julien Herzen.
  • An error in Gaussian Process filter happening with newer versions of sklearn #963 by Julien Herzen.

For developers of the library:

Fixed

0.19.0 (2022-04-13)

For users of the library:

Improved

Fixed

0.18.0 (2022-03-22)

For users of the library:

Improved

  • LinearRegressionModel and LightGBMModel can now be probabilistic, supporting quantile and poisson regression. #831, #853 by Gian Wiher.
  • New models: BATS and TBATS, based on tbats. #816 by Julien Herzen.
  • Handling of stochastic inputs in PyTorch based models. #833 by Julien Herzen.
  • GPU and TPU user guide. #826 by @gsamaras.
  • Added train and validation loss to PyTorch Lightning progress bar. #825 by Dennis Bader.
  • More losses available in darts.utils.losses for PyTorch-based models: SmapeLoss, MapeLoss and MAELoss. #845 by Julien Herzen.
  • Improvement to the seasonal decomposition #862. by Gian Wiher.
  • The gridsearch() method can now return best metric score. #822 by @nlhkh.
  • Removed needless checkpoint loading when predicting. #821 by Dennis Bader.
  • Changed default number of epochs for validation from 10 to 1. #825 by Dennis Bader.

Fixed

  • Fixed some issues with encoders in fit_from_dataset(). #829 by Julien Herzen.
  • Fixed an issue with covariates slicing for DualCovariatesForecastingModels. #858 by Dennis Bader.

0.17.1 (2022-02-17)

Patch release

For users of the library:

Fixed

  • Fixed issues with (now deprecated) torch_device_str parameter, and improved documentation related to using devices with PyTorch Lightning. #806 by Dennis Bader.
  • Fixed an issue with ReduceLROnPlateau. #806 by Dennis Bader.
  • Fixed an issue with the periodic basis functions of N-BEATS. #804 by Vladimir Chernykh.
  • Relaxed requirements for pandas; from pandas>=1.1.0 to pandas>=1.0.5. #800 by @adelnick.

0.17.0 (2022-02-15)

For users of the library:

Improved

  • 🚀 Support for PyTorch Lightning: All deep learning models are now implemented using PyTorch Lightning. This means that many more features are now available via PyTorch Lightning trainers functionalities; such as tailored callbacks, or multi-GPU training. #702 by Dennis Bader.
  • The RegressionModels now accept an output_chunk_length parameter; meaning that they can be trained to predict more than one time step in advance (and used auto-regressively to predict on longer horizons). #761 by Dustin Brunner.
  • 🔴 TimeSeries "simple statistics" methods (such as mean(), max(), min() etc, ...) have been refactored to work natively on stochastic TimeSeries, and over configurable axes. #773 by Gian Wiher.
  • 🔴 TimeSeries now support only pandas RangeIndex as an integer index, and does not support Int64Index anymore, as it became deprecated with pandas 1.4.0. This also now brings the guarantee that TimeSeries do not have missing "dates" even when indexed with integers. #777 by Julien Herzen.
  • New model: KalmanForecaster is a new probabilistic model, working on multivariate series, accepting future covariates, and which works by running the state-space model of a given Kalman filter into the future. The fit() function uses the N4SID algorithm for system identification. #743 by Julien Herzen.
  • The KalmanFilter now also works on TimeSeries containing missing values. #743 by Julien Herzen.
  • The estimators (forecasting and filtering models) now also return their own instance when calling fit(), which allows chaining calls. #741 by Julien Herzen.

Fixed

For developers of the library:

0.16.1 (2022-01-24)

Patch release

For users of the library:

For developers of the library:

0.16.0 (2022-01-13)

For users of the library:

Improved

  • The documentation page has been revamped and now contains a brand new Quickstart guide, as well as a User Guide section, which will be populated over time.
  • The API documentation has been revamped and improved, notably using numpydoc.
  • The datasets building procedure has been improved in RegressionModel, which yields dramatic speed improvements.

Added

  • The KalmanFilter can now do system identification using fit() (using nfoursid).

Fixed

For developers of the library:

  • We have switched to black for code formatting (this is checked by the CI pipeline).

0.15.0 (2021-12-24)

For users of the library:

Added:

  • On-the-fly encoding of position and calendar information in Torch-based models. Torch-based models now accept an option add_encoders parameter, specifying how to use certain calendar and position information as past and/or future covariates on the-fly.

    Example:

    from darts.dataprocessing.transformers import Scaler
add_encoders={
    'cyclic': {'future': ['month']},
    'datetime_attribute': {'past': ['hour', 'dayofweek']},
    'position': {'past': ['absolute'], 'future': ['relative']},
    'custom': {'past': [lambda idx: (idx.year - 1950) / 50]},
    'transformer': Scaler()
}

    This will add a cyclic encoding of the month as future covariates, add some datetime attributes as past and future covariates, an absolute/relative position (index), and even some custom mapping of the index (such as a function of the year). A Scaler will be applied to fit/transform all of these covariates both during training and inference.

  • The scalers can now also be applied on stochastic TimeSeries.

  • There is now a new argument max_samples_per_ts to the :func:fit() method of Torch-based models, which can be used to limit the number of samples contained in the underlying training dataset, by taking (at most) the most recent max_samples_per_ts training samples per time series.

  • All local forecasting models that support covariates (Prophet, ARIMA, VARIMA, AutoARIMA) now handle covariate slicing themselves; this means that you don't need to make sure your covariates have the exact right time span. As long as they contain the right time span, the models will slice them for you.

  • TimeSeries.map() and mappers data transformers now work on stochastic TimeSeries.

  • Granger causality function: utils.statistics.granger_causality_tests can test if one univariate TimeSeries "granger causes" another.

  • New stationarity tests for univariate TimeSeries: darts.utils.statistics.stationarity_tests, darts.utils.statistics.stationarity_test_adf and darts.utils.statistics.stationarity_test_kpss.

  • New test coverage badge 🦄

Fixed:

  • Fixed various issues in different notebooks.
  • Fixed a bug handling frequencies in Prophet model.
  • Fixed an issue causing PastCovariatesTorchModels (such as NBEATSModel) prediction to fail when n > output_chunk_length AND n not being a multiple of output_chunk_length.
  • Fixed an issue in backtesting which was causing untrained models not to be trained on the initial window when retrain=False.
  • Fixed an issue causing residuals() to fail for Torch-based models.

For developers of the library:

  • Updated the contribution guidelines
  • The unit tests have been re-organised with submodules following that of the library.
  • All relative import paths have been removed and replaced by absolute paths.
  • pytest and pytest-cov are now used to run tests and compute coverage.

0.14.0 (2021-11-28)

For users of the library:

Added:

  • Probabilistic N-BEATS: The NBEATSModel can now produce probabilistic forecasts, in a similar way as all the other deep learning models in Darts (specifying a likelihood and predicting with num_samples >> 1).
  • We have improved the speed of the data loaing functionalities for PyTorch-based models. This should speedup training, typically by a few percents.
  • Added num_loader_workers parameters to fit() and predict() methods of PyTorch-based models, in order to control the num_workers of PyTorch DataLoaders. This can sometimes result in drastic speedups.
  • New method TimeSeries.astype() which allows to easily case (e.g. between np.float64 and np.float32).
  • Added dtype as an option to the time series generation modules.
  • Added a small performance guide for PyTorch-based models.
  • Possibility to specify a (relative) time index to be used as future covariates in the TFT Model. Future covariates don't have to be specified when this is used.
  • New TFT example notebook.
  • Less strict dependencies: we have loosened the required dependencies versions.

Fixed:

  • A small fix on the Temporal Fusion Transformer TFTModel, which should improve performance.
  • A small fix in the random state of some unit tests.
  • Fixed a typo in Transformer example notebook.

0.13.1 (2021-11-08)

For users of the library:

Added:

  • Factory methods in TimeSeries are now classmethods, which makes inheritance of TimeSeries more convenient.

Fixed:

  • An issue which was causing some of the flavours installations not to work

0.13.0 (2021-11-07)

For users of the library:

Added:

  • New forecasting model: Temporal Fusion Transformer (TFTModel). A new deep learning model supporting both past and future covariates.
  • Improved support for Facebook Prophet model (Prophet):
    • Added support for fit & predict with future covariates. For instance: model.fit(train, future_covariates=train_covariates) and model.predict(n=len(test), num_sample=1, future_covariates=test_covariates)
    • Added stochastic forecasting, for instance: model.predict(n=len(test), num_samples=200)
    • Added user-defined seasonalities either at model creation with kwarg add_seasonality (Prophet(add_seasonality=kwargs_dict)) or pre-fit with model.add_seasonality(kwargs). For more information on how to add seasonalities, see the Prophet docs.
    • Added possibility to predict and return the base model's raw output with model.predict_raw(). Note that this returns a pd.DataFrame pred_df, which will not be supported for further processing with the Darts API. But it is possible to access Prophet's methods such as plots with model.model.plot_compenents(pred_df).
  • New n_random_samples in gridsearch() method, which allows to specify a number of (random) hyper parameters combinations to be tried, in order mainly to limit the gridsearch time.
  • Improvements in the checkpointing and saving of Torch models.
    • Now models don't save checkpoints by default anymore. Set save_checkpoints=True to enable them.
    • Models can be manually saved with YourTorchModel.save_model(file_path) (file_path pointing to the .pth.tar file).
    • Models can be manually loaded with YourTorchModel.load_model(file_path) or the original method YourTorchModel.load_from_checkpoint().
  • New QuantileRegression Likelihood class in darts.utils.likelihood_models. Allows to apply quantile regression loss, and get probabilistic forecasts on all deep learning models supporting likelihoods. Used by default in the Temporal Fusion Transformer.

Fixed:

  • Some issues with darts.concatenate().
  • Fixed some bugs with RegressionModels applied on multivariate series.
  • An issue with the confidence bounds computation in ACF plot.
  • Added a check for some models that do not support retrain=False for historical_forecasts().
  • Small fixes in install instructions.
  • Some rendering issues with bullet points lists in examples.

0.12.0 (2021-09-25)

For users of the library:

Added:

  • Improved probabilistic forecasting with neural networks
    • Now all neural networks based forecasting models (except NBEATSModel) support probabilistic forecasting, by providing the likelihood parameter to the model's constructor method.
    • darts.utils.likelihood_models now contains many more distributions. The complete list of likelihoods available to train neural networks based models is available here: https://unit8co.github.io/darts/generated_api/darts.utils.likelihood_models.html
    • Many of the available likelihood models now offer the possibility to specify "priors" on the distribution's parameters. Specifying such priors will regularize the training loss to make the output distribution more like the one specified by the prior parameters values.
  • Performance improvements on TimeSeries creation. creating TimeSeries is now be significantly faster, especially for large series, and filling missing dates has also been significantly sped up.
  • New rho-risk metric for probabilistic forecasts.
  • New method darts.utils.statistics.plot_hist() to plot histograms of time series data (e.g. backtest errors).
  • New argument fillna_value to TimeSeries factory methods, allowing to specify a value to fill missing dates (instead of np.nan).
  • Synthetic TimeSeries generated with darts.utils.timeseries_generation methods can now be integer-index (just pass an integer instead of a timestamp for the start argument).
  • Removed some deprecation warnings
  • Updated conda installation instructions

Fixed:

  • Removed extra 1x1 convolutions in TCN Model.
  • Fixed an issue with linewidth parameter when plotting TimeSeries.
  • Fixed a column name issue in datetime attribute time series.

For developers of the library:

  • We have removed the develop branch.
  • We force sklearn<1.0 has we have observed issues with pmdarima and sklearn==1.0

0.11.0 (2021-09-04)

For users of the library:

Added:

  • New model: LightGBMModel is a new regression model. Regression models allow to predict future values of the target, given arbitrary lags of the target as well as past and/or future covariates. RegressionModel already works with any scikit-learn regression model, and now LightGBMModel does the same with LightGBM. If you want to activate LightGBM support in Darts, please read the detailed install notes on the README carefully.
  • Added stride support to gridsearch

Fixed:

  • A bug which was causing issues when training on a GPU with a validation set
  • Some issues with custom-provided RNN modules in RNNModel.
  • Properly handle kwargs in the fit function of RegressionModels.
  • Fixed an issue which was causing problems with latest versions of Matplotlib.
  • An issue causing errors in the FFT notebook

0.10.1 (2021-08-19)

For users of the library:

Fixed:

  • A bug with memory pinning that was causing issues with training models on GPUs.

Changed:

  • Clarified conda support on the README

0.10.0 (2021-08-13)

For users of the library:

Added:

  • 🔴 Improvement of the covariates support. Before, some models were accepting a covariates (or exog) argument, but it wasn't always clear whether this represented "past-observed" or "future-known" covariates. We have made this clearer. Now all covariate-aware models support past_covariates and/or future_covariates argument in their fit() and predict() methods, which makes it clear what series is used as a past or future covariate. We recommend this article for more information and examples.

  • 🔴 Significant improvement of RegressionModel (incl. LinearRegressionModel and RandomForest). These models now support training on multiple (possibly multivariate) time series. They also support both past_covariates and future_covariates. It makes it easier than ever to fit arbitrary regression models (e.g. from scikit-learn) on multiple series, to predict the future of a target series based on arbitrary lags of the target and the past/future covariates. The signature of these models changed: It's not using "exog" keyword arguments, but past_covariates and future_covariates instead.

  • Dynamic Time Warping. There is a brand new darts.dataprocessing.dtw submodule that implements Dynamic Time Warping between two TimeSeries. It's also coming with a new dtw metric in darts.metrics. We recommend going over the new DTW example notebook for a good overview of the new functionalities

  • Conda forge installation support (fully supported with Python 3.7 only for now). You can now conda install u8darts-all.

  • TimeSeries.from_csv() allows to obtain a TimeSeries from a CSV file directly.

  • Optional cyclic encoding of the datetime attributes future covariates; for instance it's now possible to call my_series.add_datetime_attribute('weekday', cyclic=True), which will add two columns containing a sin/cos encoding of the weekday.

  • Default seasonality inference in ExponentialSmoothing. If left to None, the seasonal_periods is inferred from the freq of the provided series.

  • Various documentation improvements.

Fixed:

  • Now transformations and forecasting maintain the columns' names of the TimeSeries. The generation module darts.utils.timeseries_generation also comes with better default columns names.
  • Some issues with our Docker build process
  • A bug with GPU usage

Changed:

  • For probabilistic PyTorch based models, the generation of multiple samples (and series) at prediction time is now vectorized, which improves inference performance.

0.9.1 (2021-07-17)

For users of the library:

Added:

  • Improved GaussianProcessFilter, now handling missing values, and better handling time series indexed by datetimes.
  • Improved Gaussian Process notebook.

Fixed:

  • TimeSeries now supports indexing using pandas.Int64Index and not just pandas.RangeIndex, which solves some indexing issues.
  • We have changed all factory methods of TimeSeries to have fill_missing_dates=False by default. This is because in some cases inferring the frequency for missing dates and resampling the series is causing significant performance overhead.
  • Fixed backtesting to make it work with integer-indexed series.
  • Fixed a bug that was causing inference to crash on GPUs for some models.
  • Fixed the default folder name, which was causing issues on Windows systems.
  • We have slightly improved the documentation rendering and fixed the titles of the documentation pages for RNNModel and BlockRNNModel to distinguish them.

Changed:

  • The dependencies are not pinned to some exact versions anymore.

For developers of the library:

  • We have fixed the building process.

0.9.0 (2021-07-09)

For users of the library:

Added:

  • Multiple forecasting models can now produce probabilistic forecasts by specifying a num_samples parameter when calling predict(). Stochastic forecasts are stored by utilizing the new samples dimension in the refactored TimeSeries class (see 'Changed' section). Models supporting probabilistic predictions so far are ARIMA, ExponentialSmoothing, RNNModel and TCNModel.
  • Introduced LikelihoodModel class which is used by probabilistic TorchForecastingModel classes in order to make predictions in the form of parametrized distributions of different types.
  • Added new abstract class TorchParametricProbabilisticForecastingModel to serve as parent class for probabilistic models.
  • Introduced new FilteringModel abstract class alongside MovingAverage, KalmanFilter and GaussianProcessFilter as concrete implementations.
  • Future covariates are now utilized by TorchForecastingModels when the forecasting horizon exceeds the output_chunk_length of the model. Before, TorchForecastingModel instances could only predict beyond their output_chunk_length if they were not trained on covariates, i.e. if they predicted all the data they need as input. This restriction has now been lifted by letting a model not only consume its own output when producing long predictions, but also utilizing the covariates known in the future, if available.
  • Added a new RNNModel class which utilizes and rnn module as both encoder and decoder. This new class natively supports the use of the most recent future covariates when making a forecast. See documentation for more details.
  • Introduced optional epochs parameter to the TorchForecastingModel.predict() method which, if provided, overrides the n_epochs attribute in that particular model instance and training session.
  • Added support for TimeSeries with a pandas.RangeIndex instead of just allowing pandas.DatetimeIndex.
  • ForecastingModel.gridsearch now makes use of parallel computation.
  • Introduced a new force_reset parameter to TorchForecastingModel.__init__() which, if left to False, will prevent the user from overriding model data with the same name and directory.

Fixed:

  • Solved bug occurring when training NBEATSModel on a GPU.
  • Fixed crash when running NBEATSModel with log_tensorboard=True
  • Solved bug occurring when training a TorchForecastingModel instance with a batch_size bigger than the available number of training samples.
  • Some fixes in the documentation, including adding more details
  • Other minor bug fixes

Changed:

  • 🔴 The TimeSeries class has been refactored to support stochastic time series representation by adding an additional dimension to a time series, namely samples. A time series is now based on a 3-dimensional xarray.DataArray with shape (n_timesteps, n_components, n_samples). This overhaul also includes a change of the constructor which is incompatible with the old one. However, factory methods have been added to create a TimeSeries instance from a variety of data types, including pd.DataFrame. Please refer to the documentation of TimeSeries for more information.
  • 🔴 The old version of RNNModel has been renamed to BlockRNNModel.
  • The historical_forecast() and backtest() methods of ForecastingModel have been reorganized a bit by making use of new wrapper methods to fit and predict models.
  • Updated README.md to reflect the new additions to the library.

0.8.1 (2021-05-22)

Fixed:

  • Some fixes in the documentation

Changed:

  • The way to instantiate Dataset classes; datasets should now be used like this
from darts.datasets import AirPassengers
ts: TimeSeries = AirPassengers().load()

0.8.0 (2021-05-21)

For users of the library:

Added:

  • RandomForest algorithm implemented. Uses the scikit-learn RandomForestRegressor to predict future values from (lagged) exogenous variables and lagged values of the target.
  • darts.datasets is a new submodule allowing to easily download, cache and import some commonly used time series.
  • Better support for processing sequences of TimeSeries.
    • The Transformers, Pipelines and metrics have been adapted to be used on sequences of TimeSeries (rather than isolated series).
    • The inference of neural networks on sequences of series has been improved
  • There is a new utils function darts.utils.model_selection.train_test_split which allows to split a TimeSeries or a sequence of TimeSeries into train and test sets; either along the sample axis or along the time axis. It also optionally allows to do "model-aware" splitting, where the split reclaims as much data as possible for the training set.
  • Our implementation of N-BEATS, NBEATSModel, now supports multivariate time series, as well as covariates.

Changed

  • RegressionModel is now a user exposed class. It acts as a wrapper around any regression model with a fit() and predict() method. It enables the flexible usage of lagged values of the target variable as well as lagged values of multiple exogenous variables. Allowed values for the lags argument are positive integers or a list of positive integers indicating which lags should be used during training and prediction, e.g. lags=12 translates to training with the last 12 lagged values of the target variable. lags=[1, 4, 8, 12] translates to training with the previous value, the value at lag 4, lag 8 and lag 12.
  • 🔴 StandardRegressionModel is now called LinearRegressionModel. It implements a linear regression model from sklearn.linear_model.LinearRegression. Users who still need to use the former StandardRegressionModel with another sklearn model should use the RegressionModel now.

Fixed

  • We have fixed a bug arising when multiple scalers were used.
  • We have fixed a small issue in the TCN architecture, which makes our implementation follow the original paper more closely.

For developers of the library:

Added:

0.7.0 (2021-04-14)

Full Changelog

For users of the library:

Added:

  • darts Pypi package. It is now possible to pip install darts. The older name u8darts is still maintained and provides the different flavours for lighter installs.
  • New forecasting model available: VARIMA (Vector Autoregressive moving average).
  • Support for exogeneous variables in ARIMA, AutoARIMA and VARIMA (optional exog parameter in fit() and predict() methods).
  • New argument dummy_index for TimeSeries creation. If a series is just composed of a sequence of numbers without timestamps, setting this flag will allow to create a TimeSeries which uses a "dummy time index" behind the scenes. This simplifies the creation of TimeSeries in such cases, and makes it possible to use all forecasting models, except those that explicitly rely on dates.
  • New method TimeSeries.diff() returning differenced TimeSeries.
  • Added an example of RegressionEnsembleModel in intro notebook.

Changed:

  • Improved N-BEATS example notebook.
  • Methods TimeSeries.split_before() and split_after() now also accept integer or float arguments (in addition to timestamp) for the breaking point (e.g. specify 0.8 in order to obtain a 80%/20% split).
  • Argument value_cols no longer has to be provided if not necessary when creating a TimeSeries from a DataFrame.
  • Update of dependency requirements to more recent versions.

Fixed:

  • Fix issue with MAX_TORCH_SEED_VALUE on 32-bit architectures (unit8co#235).
  • Corrected a bug in TCN inference, which should improve accuracy.
  • Fix historical forecasts not returning last point.
  • Fixed bug when calling the TimeSeries.gaps() function for non-regular time frequencies.
  • Many small bug fixes.

0.6.0 (2021-02-02)

Full Changelog

For users of the library:

Added:

  • Pipeline.invertible() a getter which returns whether the pipeline is invertible or not.
  • TimeSeries.to_json() and TimeSeries.from_json() methods to convert TimeSeries to/from a JSON string.
  • New base class GlobalForecastingModel for all models supporting training on multiple time series, as well as covariates. All PyTorch models are now GlobalForecastingModels.
  • As a consequence of the above, the fit() function of PyTorch models (all neural networks) can optionally be called with a sequence of time series (instead of a single time series).
  • Similarly, the predict() function of these models also accepts a specification of which series should be forecasted
  • A new TrainingDataset base class.
  • Some implementations of TrainingDataset containing some slicing logic for the training of neural networks on several time series.
  • A new TimeSeriesInferenceDataset base class.
  • An implementation SimpleInferenceDataset of TimeSeriesInferenceDataset.
  • All PyTorch models have a new fit_from_dataset() method which allows to directly fit the model from a specified TrainingDataset instance (instead of using a default instance when going via the :func:fit() method).
  • A new explanatory notebooks for global models: https://github.com/unit8co/darts/blob/master/examples/02-multi-time-series-and-covariates.ipynb

Changed:

  • 🔴 removed the arguments training_series and target_series in ForecastingModels. Please consult the API documentation of forecasting models to see the new signatures.
  • 🔴 removed UnivariateForecastingModel and MultivariateForecastingModel base classes. This distinction does not exist anymore. Instead, now some models are "global" (can be trained on multiple series) or "local" (they cannot). All implementations of GlobalForecastingModels support multivariate time series out of the box, except N-BEATS.
  • Improved the documentation and README.
  • Re-ordered the example notebooks to improve the flow of examples.

Fixed:

  • Many small bug fixes.
  • Unit test speedup by about 15x.

0.5.0 (2020-11-09)

Full Changelog

For users of the library:

Added:

  • Ensemble models, a new kind of ForecastingModel which allows to ensemble multiple models to make predictions:
    • EnsembleModel is the abstract base class for ensemble models. Classes deriving from EnsembleModel must implement the ensemble() method, which takes in a List[TimeSeries] of predictions from the constituent models, and returns the ensembled prediction (a single TimeSeries object)
    • RegressionEnsembleModel, a concrete implementation of EnsembleModel which allows to specify any regression model (providing fit() and predict() methods) to use to ensemble the constituent models' predictions.
  • A new method to TorchForecastingModel: untrained_model() returns the model as it was initially created, allowing to retrain the exact same model from scratch. Works both when specifying a random_state or not.
  • New ForecastingModel.backtest() and RegressionModel.backtest() functions which by default compute a single error score from the historical forecasts the model would have produced.
    • A new reduction parameter allows to specify whether to compute the mean/median/… of errors or (when reduction is set to None) to return a list of historical errors.
    • The previous backtest() functionality still exists but has been renamed historical_forecasts()
  • Added a new last_points_only parameter to historical_forecasts(), backtest() and gridsearch()

Changed:

  • 🔴 Renamed backtest() into historical_forecasts()
  • fill_missing_values() and MissingValuesFiller used to remove the variable names when used with fill='auto' – not anymore.
  • Modified the default plotting style to increase contrast and make plots lighter.

Fixed:

  • Small mistake in the NaiveDrift model implementation which caused the first predicted value to repeat the last training value.

For developers of the library:

Changed:

  • @random_method decorator now always assigns a _random_instance field to decorated methods (seeded with a random seed). This doesn't change the observed behavior, but allows to deterministically "reset" TorchForecastingModel by saving _random_instance along with the other parameters of the model upon creation.

0.4.0 (2020-10-28)

Full Changelog

For users of the library:

Added:

  • Data (pre) processing abilities using DataTransformer, Pipeline:
    • DataTransformer provide a unified interface to apply transformations on TimeSeries, using their transform() method
    • Pipeline:
      • allow chaining of DataTransformers
      • provide fit(), transform(), fit_transform() and inverse_transform() methods.
    • Implementing your own data transformers:
      • Data transformers which need to be fitted first should derive from the FittableDataTransformer base class and implement a fit() method. Fittable transformers also provide a fit_transform() method, which fits the transformer and then transforms the data with a single call.
      • Data transformers which perform an invertible transformation should derive from the InvertibleDataTransformer base class and implement a inverse_transform() method.
      • Data transformers which are neither fittable nor invertible should derive from the BaseDataTransformer base class
      • All data transformers must implement a transform() method.
  • Concrete DataTransformer implementations:
    • MissingValuesFiller wraps around fill_missing_value() and allows to fill missing values using either a constant value or the pd.interpolate() method.
    • Mapper and InvertibleMapper allow to easily perform the equivalent of a map() function on a TimeSeries, and can be made part of a Pipeline
    • BoxCox allows to apply a BoxCox transformation to the data
  • Extended map() on TimeSeries to accept functions which use both a value and its timestamp to compute a new value e.g.f(timestamp, datapoint) = new_datapoint
  • Two new forecasting models:

Changed:

  • 🔴 Removed cols parameter from map(). Using indexing on TimeSeries is preferred. 
    # Assuming a multivariate TimeSeries named series with 3 columns or variables.
# To apply fn to columns with names '0' and '2':

#old syntax
series.map(fn, cols=['0', '2']) # returned a time series with 3 columns
#new syntax
series[['0', '2']].map(fn) # returns a time series with only 2 columns
  • 🔴 Renamed ScalerWrapper into Scaler
  • 🔴 Renamed the preprocessing module into dataprocessing
  • 🔴 Unified auto_fillna() and fillna() into a single fill_missing_value() function 
    #old syntax
fillna(series, fill=0)

#new syntax
fill_missing_values(series, fill=0)

#old syntax
auto_fillna(series, **interpolate_kwargs)

#new syntax
fill_missing_values(series, fill='auto', **interpolate_kwargs)
fill_missing_values(series, **interpolate_kwargs) # fill='auto' by default

For developers of the library

Changed:

  • GitHub release workflow is now triggered manually from the GitHub "Actions" tab in the repository, providing a #major, #minor, or #patch argument. #211
  • (A limited number of) notebook examples are now run as part of the GitHub PR workflow.

0.3.0 (2020-10-05)

Full Changelog

For users of the library:

Added:

  • Better indexing on TimeSeries (support for column/component indexing) #150
  • New FourTheta forecasting model #123, #156
  • map() method for TimeSeries #121, #166
  • Further improved the backtesting functions #111:
    • Added support for multivariate TimeSeries and models
    • Added retrain and stride parameters
  • Custom style for matplotlib plots #191
  • sMAPE metric #129
  • Option to specify a random_state at model creation using the @random_method decorator on models using neural networks to allow reproducibility of results #118

Changed:

  • 🔴 Refactored backtesting #184
    • Moved backtesting functionalities inside ForecastingModel and RegressionModel 
      # old syntax:
backtest_forecasting(forecasting_model, *args, **kwargs)

# new syntax:
forecasting_model.backtest(*args, **kwargs)

# old syntax:
backtest_regression(regression_model, *args, **kwargs)

# new syntax:
regression_model.backtest(*args, **kwargs)
    • Consequently removed the backtesting module
  • 🔴 ForecastingModel fit() method syntax using TimeSeries indexing instead of additional parameters #161 
    # old syntax:
multivariate_model.fit(multivariate_series, target_indices=[0, 1])

# new syntax:
multivariate_model.fit(multivariate_series, multivariate_series[["0", "1"]])

# old syntax:
univariate_model.fit(multivariate_series, component_index=2)

# new syntax:
univariate_model.fit(multivariate_series["2"])

Fixed:

  • Solved issue of TorchForecastingModel.predict(n) throwing an error at n=1. #108
  • Fixed MASE metrics #129
  • [BUG] ForecastingModel.backtest: Can bypass sanity checks #188
  • ForecastingModel.backtest() fails if forecast_horizon isn't provided #186

For developers of the library

Added:

  • Gradle to build docs, docker image, run tests, … #112, #127, #159
  • M4 competition benchmark and notebook to the examples #138
  • Check of test coverage #141

Changed:

  • Dependencies' versions are now fixed #173
  • Workflow: tests trigger on Pull Request #165

Fixed:

  • Passed the freq parameter to the TimeSeries constructor in all TimeSeries generating functions #157

Older releases

Full Changelog