Package: sktime-legacy
sktimeAscikit-learncompatible Python toolbox for learning with
time series. sktime currently supports:State-of-the-art time series classification and regression algorithms,Classical forecasting including reduction strategies to regression,Benchmarking and post-hoc evaluation methods based onmlaut.For deep learning methods, we have a separate extension package:sktime-dl.sktime is under active development and we are looking forcontributors. You can find our developmentroadmapbelow.InstallationThe package is available via PyPI using:pip install sktimeBut note that the package is actively being developed and currently not feature stable.Development versionTo install the development version, please see ouradvanced installation instructions.DocumentationTutorials can be found in theexamplesfolder or can be launched directly onBinder.The full API documentation can be foundhere.Overviewsktime extends the standard scikit-learn API to handle modular workflows for time series and panel data.
The goal is to create a unified interface for various distinct but closely related learning tasks that arise in a temporal data context, such as time series classification or forecasting.
To find our more, take a look at ourpaper.Currently, the package implements:Various state-of-the-art algorithms for time series classification and regression,Transformers, including series-to-series transforms (e.g. Fourier transform), series-to-primitives transforms a.k.a. feature extractors (e.g. mean, variance), sub-divided into fittables (on table) and row-wise applicates,Pipelining, allowing to chain multiple transformers with a final estimator,Meta-estimators such as reduction strategies, grid-search tuners and ensembles, including ensembles for multivariate time series classification,Composite strategies, such as a fully customisable random forest for time-series classification, with interval segmentation and feature extraction,Classical forecasting algorithms and reduction strategies to solve forecasting tasks with time series regression algorithms.In addition, sktime includes a high-level API that unifies multiple learning tasks, partially inspired by the APIs ofmlrandopenML.
In particular, we introduce:Task objects that encapsulate meta-data from a dataset and the necessary information about the particular learning task, e.g. the instructions on how to derive the target/labels for classification from the data,Strategy objects that wrap estimators and allow to call fit and predict methods using data and a task object.Development road mapDevelopment of a time series annotation framework, including segmentation and outlier detection,Integration of supervised/panel forecasting based on a modifiedpysfAPI,Unsupervised methods including time series clustering,Design and implementation of a specialised data container for efficient handling of time series/panel data in a modelling workflow and separation of time series meta-data,Development of a probabilistic modelling framework for time series, including survival and point process models based on an adaptedskprointerface.ContributionsWe are actively looking for contributors. Please contact @fkiraly or @jasonlines for volunteering or information on
paid opportunities, or simplychatwith us
orraise an issue.Please also take a look at ourCode of Conductandcontributing guidelines.Former and current contributors to the API design and project management include:API design: Anthony Bagnall, Sajaysurya Ganesh, Viktor Kazakov, Franz Király, Jason Lines, Markus LöningProject management: Anthony Bagnall, Franz Király, Jason Lines, Markus LöningHow to cite sktimeIf you use sktime in a scientific publication, we would appreciate citations to the following paper:Markus Löning, Anthony Bagnall, Sajaysurya Ganesh, Viktor Kazakov, Jason Lines: “sktime: A Unified Interface for Machine Learning with Time Series”, 2019; arXiv:1909.07872Bibtex entry:@misc{sktime,
      author = {Markus Löning and Anthony Bagnall and Sajaysurya Ganesh and Viktor Kazakov
      and Jason Lines and Franz J. Király},
      title = {sktime: A Unified Interface for Machine Learning with Time Series},
      year = {2019},
      eprint = {arXiv:1909.07872},
}