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Forecasting with sktime: Designing sktime's New Forecasting API and Applying It to Replicate and Extend the M4 Study

by   Markus Löning, et al.

We present a new open-source framework for forecasting in Python. Our framework forms part of sktime, a machine learning toolbox with a unified interface for different time series learning tasks, like forecasting, but also time series classification and regression. We provide a dedicated forecasting interface, common statistical algorithms, and scikit-learn compatible tools for building composite machine learning models. We use sktime to both replicate key results from the M4 forecasting study and to extend it. sktime allows to easily build, tune and evaluate new models. We investigate the potential of common machine learning techniques for univariate forecasting, including reduction, boosting, ensembling, pipelining and tuning. We find that simple hybrid models can boost the performance of statistical models, and that pure machine learning models can achieve competitive forecasting performance on the hourly data sets, outperforming the statistical algorithms and coming close to the M4 winner model.


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