Why? Existing tools are not well-suited to time series tasks and do not easily integrate together. Methods in the scikit-learn package assume that data is structured in a tabular format and each column is i.i.d. Packages containing time series learning modules, such as statsmodels, do not integrate well together. Further, many essential time series operations, such as splitting data into train and test sets across time, are not available in existing python packages. To address these challenges, sktime was created.

Time series forecasting is ubiquitous in real-world applications. Examples include forecasting of demand to fill up inventories, economic growth forecasts to inform policies, and predicting stock prices to guide financial decisions. Forecasting is also a fruitful area for machine learning research, and pure and hybrid machine learning approaches have recently achieved state-of-the-art performance [1, 2]. In practice, forecasting involves a number of steps: we first need to specify, fit and select an appropriate model, and then evaluate and deploy it. There are various open-source toolboxes that help us implement these steps. However, most existing toolboxes are limited in important respects.

Our goal is to extend existing machine learning capabilities, most notably scik it-learn [16], to the temporal data setting by providing a unified interface for several time series learning tasks. Time series data is ubiquitous in many applications. Exampl es include sensor readings from industrial processes, spectroscopy wave length data from chemical samples, or bed -side monitor medical data from patients. There is a broad variety of distinct but closely related learning task s that arise in such contexts, including time series classifi cation, forecasting and annotation among others.

sktime is an open source, Python based, sklearn compatible toolkit for time series analysis developed by researchers at the University of East Anglia, University College London and the Alan Turing Institute. A key initial goal for sktime was to provide time series classification functionality equivalent to that available in a related java package, tsml. We describe the implementation of six such classifiers in sktime and compare them to their tsml equivalents. We demonstrate correctness through equivalence of accuracy on a range of standard test problems and compare the build time of the different implementations. We find that there is significant difference in accuracy on only one of the six algorithms, and this difference was to be expected. We found a much wider range of difference in efficiency. Again, this was not unexpected, but it does highlight ways both toolkits could be improved. PLEASE NOTE THIS PAPER IS NOT COMPLETE. It is a work in progress and we have pushed it early so that we can reference it in another paper. More to follow!