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Changelog

Darts is still in an early development phase and we cannot always guarantee backwards compatibility. Changes that may break code which uses a previous release of Darts are marked with a "🔴".

Unreleased

Full Changelog

0.15.0 (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'], 'future': ['year', 'dayofweek']},
    'position': {'past': ['absolute'], 'future': ['relative']},
    'custom': {'past': [lambda index: (index.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 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 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