Package: skytime
Welcome to sktimeA unified interface for machine learning with time series:rocket:Version 0.16.1 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 time series classification, regression, clustering, annotation and forecasting. It comes withtime series algorithmsandscikit-learncompatible tools to build, tune and validate time series models.OverviewCI/CDCodeDownloadsCommunityCitation:books: DocumentationDocumentation:star:TutorialsNew to sktime? Here's everything you need to know!:clipboard:Binder NotebooksExample notebooks to play with in your browser.:woman_technologist:User GuidesHow to use sktime and its features.:scissors:Extension TemplatesHow to build your own estimator using sktime's API.:control_knobs:API ReferenceThe detailed reference for sktime's API.:tv:Video TutorialOur video tutorial from 2021 PyData Global.:hammer_and_wrench:ChangelogChanges and version history.:deciduous_tree:Roadmapsktime's software and community development plan.:pencil:Related SoftwareA list of related software.:speech_balloon: Where to ask questionsQuestions and feedback are extremely welcome! Please understand that we won't be able to provide individual support via email. We also believe that help is much more valuable if it's shared publicly, so that more people can benefit from it.TypePlatforms:bug:Bug ReportsGitHub Issue Tracker:sparkles:Feature Requests & IdeasGitHub Issue Tracker:woman_technologist:Usage QuestionsGitHub Discussions·Stack Overflow:speech_balloon:General DiscussionGitHub Discussions:factory:Contribution & Development[Slack], contributors channel ·Discord:globe_with_meridians:Community collaboration sessionDiscord- Fridays 1pm UTC, dev/meet-ups channel:dizzy: FeaturesOur aim is to make the time series analysis ecosystem more interoperable and usable as a whole. sktime provides aunified interface for distinct but related time series learning tasks. It featuresdedicated time series algorithmsandtools for composite model buildingincluding pipelining, ensembling, tuning and reduction that enables users to apply an algorithm for one task to another.sktime also providesinterfaces to related libraries, for examplescikit-learn,statsmodels,tsfresh,PyODandfbprophet, among others.Fordeep learning, see our companion package:sktime-dl.ModuleStatusLinksForecastingstableTutorial·API Reference·Extension TemplateTime Series ClassificationstableTutorial·API Reference·Extension TemplateTime Series RegressionstableAPI ReferenceTransformationsstableAPI Reference·Extension TemplateTime Series ClusteringmaturingExtension TemplateTime Series Distances/KernelsexperimentalExtension TemplateAnnotationexperimentalExtension Template:hourglass_flowing_sand: Install sktimeFor trouble shooting and detailed installation instructions, see thedocumentation.Operating system: macOS X · Linux · Windows 8.1 or higherPython version: Python 3.7, 3.8, 3.9, 3.10, and 3.11 (only 64 bit)Package managers:pip·conda(viaconda-forge)pipUsing pip, sktime releases are available as source packages and binary wheels. You can see all available wheelshere.pipinstallsktimeor, with maximum dependencies,pipinstallsktime[all_extras]condaYou can also install sktime fromcondavia theconda-forgechannel. For the feedstock including the build recipe and configuration, check outthis repository.condainstall-cconda-forgesktimeor, with maximum dependencies,condainstall-cconda-forgesktime-all-extras:zap: QuickstartForecastingfromsktime.datasetsimportload_airlinefromsktime.forecasting.baseimportForecastingHorizonfromsktime.forecasting.model_selectionimporttemporal_train_test_splitfromsktime.forecasting.thetaimportThetaForecasterfromsktime.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)>>>0.08661467738190656Time Series Classificationfromsktime.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:wave: How to get involvedThere are many ways to join the sktime community. We follow theall-contributorsspecification: all kinds of contributions are welcome - not just code.Documentation:gift_heart:ContributeHow to contribute to sktime.:school_satchel:MentoringNew to open source? Apply to our mentoring program!:date:MeetingsJoin our discussions, tutorials, workshops and sprints!:woman_mechanic:Developer GuidesHow to further develop sktime's code base.:construction:Enhancement ProposalsDesign a new feature for sktime.:medal_sports:ContributorsA list of all contributors.:raising_hand:RolesAn overview of our core community roles.:money_with_wings:DonateFund sktime maintenance and development.:classical_building:GovernanceHow and by whom decisions are made in sktime's community.:bulb: Project visionby 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 ecosystemsandprovider of interoperable interfaces-- interoperable withscikit-learn,statsmodels,tsfresh, and other community favourites.rich model composition and reduction functionality-- build tuning and feature extraction pipelines, solve forecasting tasks withscikit-learnregressors.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, avoid pitfalls.easily extensible-- easy extension templates to add your own algorithms compatible with sktime's API.