Package: darts
Time Series Made Easy in PythonDartsis a Python library for user-friendly forecasting and anomaly detection
on time series. It contains a variety of models, from classics such as ARIMA to
deep neural networks. The forecasting models can all be used in the same way,
usingfit()andpredict()functions, similar to scikit-learn.
The library also makes it easy to backtest models,
combine the predictions of several models, and take external data into account.
Darts supports both univariate and multivariate time series and models.
The ML-based models can be trained on potentially large datasets containing multiple time
series, and some of the models offer a rich support for probabilistic forecasting.Darts also offers extensive anomaly detection capabilities.
For instance, it is trivial to apply PyOD models on time series to obtain anomaly scores,
or to wrap any of Darts forecasting or filtering models to obtain fully
fledged anomaly detection models.DocumentationQuickstartUser GuideAPI ReferenceExamplesHigh Level IntroductionsIntroductory Blog PostIntroduction video (25 minutes)Articles on Selected TopicsTraining Models on Multiple Time SeriesUsing Past and Future CovariatesTemporal Convolutional Networks and ForecastingProbabilistic ForecastingTransfer Learning for Time Series ForecastingHierarchical Forecast ReconciliationQuick InstallWe recommend to first setup a clean Python environment for your project with Python 3.8+ using your favorite tool
(conda,venv,virtualenvwith
or withoutvirtualenvwrapper).Once your environment is set up you can install darts using pip:pip install dartsFor more details you can refer to ourinstallation instructions.Example UsageForecastingCreate aTimeSeriesobject from a Pandas DataFrame, and split it in train/validation series:importpandasaspdfromdartsimportTimeSeries# Read a pandas DataFramedf=pd.read_csv("AirPassengers.csv",delimiter=",")# Create a TimeSeries, specifying the time and value columnsseries=TimeSeries.from_dataframe(df,"Month","#Passengers")# Set aside the last 36 months as a validation seriestrain,val=series[:-36],series[-36:]Fit an exponential smoothing model, and make a (probabilistic) prediction over the validation series' duration:fromdarts.modelsimportExponentialSmoothingmodel=ExponentialSmoothing()model.fit(train)prediction=model.predict(len(val),num_samples=1000)Plot the median, 5th and 95th percentiles:importmatplotlib.pyplotaspltseries.plot()prediction.plot(label="forecast",low_quantile=0.05,high_quantile=0.95)plt.legend()Anomaly DetectionLoad a multivariate series, trim it, keep 2 components, split train and validation sets:fromdarts.datasetsimportETTh2Datasetseries=ETTh2Dataset().load()[:10000][["MUFL","LULL"]]train,val=series.split_before(0.6)Build a k-means anomaly scorer, train it on the train set
and use it on the validation set to get anomaly scores:fromdarts.adimportKMeansScorerscorer=KMeansScorer(k=2,window=5)scorer.fit(train)anom_score=scorer.score(val)Build a binary anomaly detector and train it over train scores,
then use it over validation scores to get binary anomaly classification:fromdarts.adimportQuantileDetectordetector=QuantileDetector(high_quantile=0.99)detector.fit(scorer.score(train))binary_anom=detector.detect(anom_score)Plot (shifting and scaling some of the series
to make everything appear on the same figure):importmatplotlib.pyplotaspltseries.plot()(anom_score/2.-100).plot(label="computed anomaly score",c="orangered",lw=3)(binary_anom*45-150).plot(label="detected binary anomaly",lw=4)FeaturesForecasting Models:A large collection of forecasting models; from statistical models (such as
ARIMA) to deep learning models (such as N-BEATS). Seetable of models below.Anomaly DetectionThedarts.admodule contains a collection of anomaly scorers,
detectors and aggregators, which can all be combined to detect anomalies in time series.
It is easy to wrap any of Darts forecasting or filtering models to build
a fully fledged anomaly detection model that compares predictions with actuals.
ThePyODScorermakes it trivial to use PyOD detectors on time series.Multivariate Support:TimeSeriescan be multivariate - i.e., contain multiple time-varying
dimensions instead of a single scalar value. Many models can consume and produce multivariate series.Multiple series training (global models):All machine learning based models (incl. all neural networks)
support being trained on multiple (potentially multivariate) series. This can scale to large datasets too.Probabilistic Support:TimeSeriesobjects can (optionally) represent stochastic
time series; this can for instance be used to get confidence intervals, and many models support different
flavours of probabilistic forecasting (such as estimating parametric distributions or quantiles).
Some anomaly detection scorers are also able to exploit these predictive distributions.Past and Future Covariates support:Many models in Darts support past-observed and/or future-known
covariate (external data) time series as inputs for producing forecasts.Static Covariates support:In addition to time-dependent data,TimeSeriescan also contain
static data for each dimension, which can be exploited by some models.Hierarchical Reconciliation:Darts offers transformers to perform reconciliation.
These can make the forecasts add up in a way that respects the underlying hierarchy.Regression Models:It is possible to plug-in any scikit-learn compatible model
to obtain forecasts as functions of lagged values of the target series and covariates.Explainability:Darts has the ability toexplainsome forecasting models using Shap values.Data processing:Tools to easily apply (and revert) common transformations on
time series data (scaling, filling missing values, differencing, boxcox, ...)Metrics:A variety of metrics for evaluating time series' goodness of fit;
from R2-scores to Mean Absolute Scaled Error.Backtesting:Utilities for simulating historical forecasts, using moving time windows.PyTorch Lightning Support:All deep learning models are implemented using PyTorch Lightning,
supporting among other things custom callbacks, GPUs/TPUs training and custom trainers.Filtering Models:Darts offers three filtering models:KalmanFilter,GaussianProcessFilter,
andMovingAverageFilter, which allow to filter time series, and in some cases obtain probabilistic
inferences of the underlying states/values.DatasetsThedarts.datasetssubmodule contains some popular time series datasets for rapid
and reproducible experimentation.Forecasting ModelsHere's a breakdown of the forecasting models currently implemented in Darts. We are constantly working
on bringing more models and features.ModelSourcesTarget Series Support:Univariate/MultivariateCovariates Support:Past-observed/Future-known/StaticProbabilistic Forecasting:Sampled/Distribution ParametersTraining & Forecasting on Multiple SeriesBaseline Models(LocalForecastingModel)NaiveMean🟩 🟩🟥 🟥 🟥🟥 🟥🟥NaiveSeasonal🟩 🟩🟥 🟥 🟥🟥 🟥🟥NaiveDrift🟩 🟩🟥 🟥 🟥🟥 🟥🟥NaiveMovingAverage🟩 🟩🟥 🟥 🟥🟥 🟥🟥Statistical / Classic Models(LocalForecastingModel)ARIMA🟩 🟥🟥 🟩 🟥🟩 🟥🟥VARIMA🟥 🟩🟥 🟩 🟥🟩 🟥🟥AutoARIMA🟩 🟥🟥 🟩 🟥🟥 🟥🟥StatsForecastAutoArima(faster AutoARIMA)Nixtla's statsforecast🟩 🟥🟥 🟩 🟥🟩 🟥🟥ExponentialSmoothing🟩 🟥🟥 🟥 🟥🟩 🟥🟥StatsforecastAutoETSNixtla's statsforecast🟩 🟥🟥 🟩 🟥🟩 🟥🟥StatsforecastAutoCESNixtla's statsforecast🟩 🟥🟥 🟥 🟥🟥 🟥🟥BATSandTBATSTBATS paper🟩 🟥🟥 🟥 🟥🟩 🟥🟥ThetaandFourThetaTheta&4 Theta🟩 🟥🟥 🟥 🟥🟥 🟥🟥StatsForecastAutoThetaNixtla's statsforecast🟩 🟥🟥 🟥 🟥🟩 🟥🟥ProphetProphet repo🟩 🟥🟥 🟩 🟥🟩 🟥🟥FFT(Fast Fourier Transform)🟩 🟥🟥 🟥 🟥🟥 🟥🟥KalmanForecasterusing the Kalman filter and N4SID for system identificationN4SID paper🟩 🟩🟥 🟩 🟥🟩 🟥🟥Crostonmethod🟩 🟥🟥 🟥 🟥🟥 🟥🟥Regression Models(GlobalForecastingModel)RegressionModel: generic wrapper around any sklearn regression model🟩 🟩🟩 🟩 🟩🟥 🟥🟩LinearRegressionModel🟩 🟩🟩 🟩 🟩🟩 🟩🟩RandomForest🟩 🟩🟩 🟩 🟩🟥 🟥🟩LightGBMModel🟩 🟩🟩 🟩 🟩🟩 🟩🟩XGBModel🟩 🟩🟩 🟩 🟩🟩 🟩🟩CatBoostModel🟩 🟩🟩 🟩 🟩🟩 🟩🟩PyTorch (Lightning)-based Models(GlobalForecastingModel)RNNModel(incl. LSTM and GRU); equivalent to DeepAR in its probabilistic versionDeepAR paper🟩 🟩🟥 🟩 🟥🟩 🟩🟩BlockRNNModel(incl. LSTM and GRU)🟩 🟩🟩 🟥 🟥🟩 🟩🟩NBEATSModelN-BEATS paper🟩 🟩🟩 🟥 🟥🟩 🟩🟩NHiTSModelN-HiTS paper🟩 🟩🟩 🟥 🟥🟩 🟩🟩TCNModelTCN paper,DeepTCN paper,blog post🟩 🟩🟩 🟥 🟥🟩 🟩🟩TransformerModel🟩 🟩🟩 🟥 🟥🟩 🟩🟩TFTModel(Temporal Fusion Transformer)TFT paper,PyTorch Forecasting🟩 🟩🟩 🟩 🟩🟩 🟩🟩DLinearModelDLinear paper🟩 🟩🟩 🟩 🟩🟩 🟩🟩NLinearModelNLinear paper🟩 🟩🟩 🟩 🟩🟩 🟩🟩TiDEModelTiDE paper🟩 🟩🟩 🟩 🟩🟩 🟩🟩Ensemble Models(GlobalForecastingModel): Model support is dependent on ensembled forecasting models and the ensemble model itselfNaiveEnsembleModel🟩 🟩🟩 🟩 🟩🟩 🟩🟩RegressionEnsembleModel🟩 🟩🟩 🟩 🟩🟩 🟩🟩Community & ContactAnyone is welcome to join ourGitter roomto ask questions, make proposals,
discuss use-cases, and more. If you spot a bug or have suggestions, GitHub issues are also welcome.If what you want to tell us is not suitable for Gitter or Github,
feel free to send us an email atdarts@unit8.cofor
darts related matters orinfo@unit8.cofor any other
inquiries.ContributeThe development is ongoing, and we welcome suggestions, pull requests and issues on GitHub.
All contributors will be acknowledged on thechange log page.Before working on a contribution (a new feature or a fix),check our contribution guidelines.CitationIf you are using Darts in your scientific work, we would appreciate citations to the following JMLR paper.Darts: User-Friendly Modern Machine Learning for Time SeriesBibtex entry:@article{JMLR:v23:21-1177,
  author  = {Julien Herzen and Francesco LÃ¤ssig and Samuele Giuliano Piazzetta and Thomas Neuer and LÃ©o Tafti and Guillaume Raille and Tomas Van Pottelbergh and Marek Pasieka and Andrzej Skrodzki and Nicolas Huguenin and Maxime Dumonal and Jan KoÅ›cisz and Dennis Bader and FrÃ©dÃ©rick Gusset and Mounir Benheddi and Camila Williamson and Michal Kosinski and Matej Petrik and GaÃ«l Grosch},
  title   = {Darts: User-Friendly Modern Machine Learning for Time Series},
  journal = {Journal of Machine Learning Research},
  year    = {2022},
  volume  = {23},
  number  = {124},
  pages   = {1-6},
  url     = {http://jmlr.org/papers/v23/21-1177.html}
}