About us¶
Mission statement¶
sktime enables understandable and composable machine learning with time series. It provides scikit-learn compatible algorithms and model composition tools, supported by a clear taxonomy of learning tasks, with instructive documentation and a friendly community.
History¶
sktime was started in April 2019 as a collaborative project between Franz Király, Markus Löning, Tony Bagnall and Jason Lines. In the first year, it grew into a community-driven project with contributions from researchers and practitioners from around the globe.
Authors¶
For an overview of current and past contributors, please see our contributors page.
Citing sktime¶
If you use sktime in a scientific publication, we would appreciate citations to the following paper:
Funding¶
sktime is a community-driven project, however institutional and private grants help to assure its sustainability.
We would like to thank the following funders.
The Alan Turing Institute funded three months of the initial development under the UKRI Strategic Priorities Fund (EPSRC grant no EP/T001569/1), particularly the Tools, Practices and Systems theme within that grant.
Markus Löning’s contribution was supported by the UK Economic and Social Research Council (ESRC), the Consumer Data Research Centre (CDRC), and the Enrichment Scheme at the The Alan Turing Institute.
Sprints¶
The 2019 joint sktime MLJ development sprint was kindly hosted by UCL and The Alan Turing Institute. Some participants could attend thanks to the initial funding of the The Alan Turing Institute.
Infrastructure support¶
We would also like to thank Microsoft Azure, GitHub Actions, and AppVeyor, ReadtheDocs for the free computing time on their Continuous Integration servers.