pyWATTS: Python Workflow Automation Tool for Time Series
This tool addresses the challenge for researchers in time series analysis who struggle with unclear APIs and lack of documentation, though it is incremental as it builds on existing workflow automation concepts.
The authors tackled the problem of integrating and replicating time series analysis methods by developing pyWATTS, an open-source Python workflow automation tool that enables seamless integration, subpipelining for repetitive tasks, and support for key machine learning libraries.
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.