mlsanityData to 14 Oct 2026

pyWATTS: Python Workflow Automation Tool for Time Series

arXiv 2106.10157 ↗First posted 18 Jun 2021cs.LGcited by 9

Not on the map. Why?

Abstract (arXiv)

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 textttpyWATTS, 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.

Key facts. Cited by 9 papers in the map (data through 14 Oct 2026). Builds on 3 of its 10 references that are in the map. First posted on arXiv 18 Jun 2021.

Builds on

3 of 10 references.

References · 3

  1. 2019PyTorch: An Imperative Style, High-Performance Deep Learning Librarycited by 20,305
  2. 2020River: machine learning for streaming data in Pythoncited by 98
  3. 2021Data-Driven Copy-Paste Imputation for Energy Time Seriescited by 4

Built on by

Cited by year

Citing papers per year: 2021: 3; 2022: 3; 2023: 3.

9 papers.