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A scikit-learn compatible 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 on mlaut.

For deep learning methods, we have a separate extension package: sktime-dl.

sktime is under active development and we are looking for contributors. You can find our development road map below.

The package is available via PyPI using:

pip install sktime

But note that the package is actively being developed and currently not feature stable.

To install the development version, please see our advanced installation instructions.

• Tutorials can be found in the examples folder or can be launched directly on Binder.
• The full API documentation can be found here.

sktime 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 our paper.

Currently, the package implements:

• Various state-of-the-art algorithms for time series classification and regression, ported from the Java-based tsml toolkit,
• 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 of mlr and openML. 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.

1. Development of a time series annotation framework, including segmentation and outlier detection,
2. Integration of supervised/panel forecasting based on a modified pysf API,
3. Unsupervised methods including time series clustering,
4. 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,
5. Development of a probabilistic modelling framework for time series, including survival and point process models based on an adapted skpro interface.

We are actively looking for contributors. Please contact @fkiraly or @jasonlines for volunteering or information on paid opportunities, or simply chat with us or raise an issue.

Please also take a look at our Code of Conduct and contributing 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 Löning
• Project management: Anthony Bagnall, Franz Király, Jason Lines, Markus Löning

If 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:1909.07872

Bibtex entry:

@inproceedings{sktime,
    author = {L{\"{o}}ning, Markus and Bagnall, Anthony and Ganesh, Sajaysurya and Kazakov, Viktor and Lines, Jason and Kir{\'{a}}ly, Franz J},
    booktitle = {Workshop on Systems for ML at NeurIPS 2019},
    title = {{sktime: A Unified Interface for Machine Learning with Time Series}},
    date = {2019},
}