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.
翻译:时间序列数据对于从金融市场到能源系统等各种应用都至关重要。由于时间序列分析工具和方法的重要性,用于时间序列分析的工具和方法的数量和复杂性正在不断增加。然而,由于文件不明确,研究人员很难将数据纳入他们的研究项目和复制结果。此外,在时间序列分析中,存在许多重复性任务,经常为每个项目重新实施,不必要地花费时间。为了解决这些问题,我们提出了一个基于开放源的Python软件包,这是一个用于分析时间序列数据的非序列工作流程自动化工具。 PyWATS包含模块,有明确界定的界面,以便能够无缝地整合新的或现有方法,分管道可以容易地复制重复性任务,负载和保存功能以简单复制结果,以及对关键Python机器学习图书馆,如Scikit-learn、PyTorch和Keras的本地支持。