sktime is a new Python toolbox for machine learning with time series and, to the best of our knowledge, the first unified toolbox for time series. Our ambition is to provide for time series what scikit-learn provides for tabular data. This involves extending scikit-learn to the different time series learning tasks, such as time series classification, clustering, forecasting and anomaly detection. To find out more, check out our paper published at the Workshop on Systems for ML at NeurIPS 2019.
What we are looking for
We’re actively looking for contributors and your help is extremely welcome. Therefore, if
- you are interested in time series, machine learning (ML), statistics, API design and software architecture,
- you like coding in Python,
- you are familiar with the basic data science ecosystem in Python, including numpy, pandas and scikit-learn,
- you enjoy working with a vibrant team of experienced ML scientists and software engineers,
- you always wanted to join an open-source community,
then GSoC with sktime is for you! You’ll spend the summer working with our enthusiastic and open-minded team of developers who are creating one of the first comprehensive time series ML toolboxes out there.
9.1 Time series regression
9.2 Time series clustering
9.4 Develop a new module
(See also projects 9.4 and 9.5, separate posts)
Read more, including closer instructions to students, on the sktime Github GSoc page.
Mentors: Markus Löning, Tony Bagnall, Franz Király, Martina Vilas @martinagvilas