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A unified interface for machine learning with time series

🚀Version 0.36.0 out now!Check out the release notes here.

sktime is a library for time series analysis in Python. It provides a unified interface for multiple time series learning tasks. Currently, this includes forecasting, time series classification, clustering, anomaly/changepoint detection, and other tasks. It comes withtime series algorithms andscikit-learn compatible tools to build, tune, and validate time series models.

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Questions and feedback are extremely welcome! We strongly believe in the value of sharing help publicly, as it allows a wider audience to benefit from it.

Our objective is to enhance the interoperability and usability of the time series analysis ecosystem in its entirety. sktime provides aunified interface for distinct but related time series learning tasks. It featuresdedicated time series algorithms andtools for composite model building, such as pipelining, ensembling, tuning, and reduction, empowering users to apply algorithms designed for one task to another.

sktime also providesinterfaces to related libraries, for examplescikit-learn,statsmodels,tsfresh,PyOD, andfbprophet, among others.

For troubleshooting and detailed installation instructions, see thedocumentation.

• Operating system: macOS X · Linux · Windows 8.1 or higher
• Python version: Python 3.8, 3.9, 3.10, 3.11, and 3.12 (only 64-bit)
• Package managers:pip ·conda (viaconda-forge)

Using pip, sktime releases are available as source packages and binary wheels.Available wheels are listedhere.

pip install sktime

or, with maximum dependencies,

pip install sktime[all_extras]

For curated sets of soft dependencies for specific learning tasks:

pip install sktime[forecasting]# for selected forecasting dependenciespip install sktime[forecasting,transformations]# forecasters and transformers

or similar. Valid sets are:

• forecasting
• transformations
• classification
• regression
• clustering
• param_est
• networks
• detection
• alignment

Cave: in general, not all soft dependencies for a learning task are installed,only a curated selection.

You can also install sktime fromconda via theconda-forge channel.The feedstock including the build recipe and configuration is maintainedinthis conda-forge repository.

conda install -c conda-forge sktime

or, with maximum dependencies,

conda install -c conda-forge sktime-all-extras

(asconda does not support dependency sets,flexible choice of soft dependencies is unavailable viaconda)

fromsktime.datasetsimportload_airlinefromsktime.forecasting.baseimportForecastingHorizonfromsktime.forecasting.thetaimportThetaForecasterfromsktime.splitimporttemporal_train_test_splitfromsktime.performance_metrics.forecastingimportmean_absolute_percentage_errory=load_airline()y_train,y_test=temporal_train_test_split(y)fh=ForecastingHorizon(y_test.index,is_relative=False)forecaster=ThetaForecaster(sp=12)# monthly seasonal periodicityforecaster.fit(y_train)y_pred=forecaster.predict(fh)mean_absolute_percentage_error(y_test,y_pred)>>>0.08661467738190656

fromsktime.classification.interval_basedimportTimeSeriesForestClassifierfromsktime.datasetsimportload_arrow_headfromsklearn.model_selectionimporttrain_test_splitfromsklearn.metricsimportaccuracy_scoreX,y=load_arrow_head()X_train,X_test,y_train,y_test=train_test_split(X,y)classifier=TimeSeriesForestClassifier()classifier.fit(X_train,y_train)y_pred=classifier.predict(X_test)accuracy_score(y_test,y_pred)>>>0.8679245283018868

There are many ways to join the sktime community. We follow theall-contributors specification: all kinds of contributions are welcome - not just code.

Thanks to all our community for all your wonderful contributions, PRs, issues, ideas.

• By the community, for the community -- developed by a friendly and collaborative community.
• Theright tool for the right task -- helping users to diagnose their learning problem and suitable scientific model types.
• Embedded in state-of-art ecosystems andprovider of interoperable interfaces -- interoperable withscikit-learn,statsmodels,tsfresh, and other community favorites.
• Rich model composition and reduction functionality -- build tuning and feature extraction pipelines, solve forecasting tasks withscikit-learn regressors.
• Clean, descriptive specification syntax -- based on modern object-oriented design principles for data science.
• Fair model assessment and benchmarking -- build your models, inspect your models, check your models, and avoid pitfalls.
• Easily extensible -- easy extension templates to add your own algorithms compatible with sktime's API.