  Time series data are fundamental for a variety of applications, ranging from
financial markets to energy systems. Due to their importance, the number and
complexity of tools and methods used for time series analysis is constantly
increasing. However, due to unclear APIs and a lack of documentation,
researchers struggle to integrate them into their research projects and
replicate results. Additionally, in time series analysis there exist many
repetitive tasks, which are often re-implemented for each project,
unnecessarily costing time. To solve these problems we present
\texttt{pyWATTS}, an open-source Python-based package that is a non-sequential
workflow automation tool for the analysis of time series data. pyWATTS includes
modules with clearly defined interfaces to enable seamless integration of new
or existing methods, subpipelining to easily reproduce repetitive tasks, load
and save functionality to simply replicate results, and native support for key
Python machine learning libraries such as scikit-learn, PyTorch, and Keras.
