pyWATTS: Python Workflow Automation Tool for Time Series
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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
- 2019PyTorch: An Imperative Style, High-Performance Deep Learning Librarycited by 20,305
- 2020River: machine learning for streaming data in Pythoncited by 98
- 2021Data-Driven Copy-Paste Imputation for Energy Time Seriescited by 4
Built on by
Citing papers per year: 2021: 3; 2022: 3; 2023: 3.
9 papers.
- 2022Review of automated time series forecasting pipelinescited by 18
- 2021Concepts for Automated Machine Learning in Smart Grid Applicationscited by 4
- 2023ExeKGLib: Knowledge Graphs-Empowered Machine Learning Analyticscited by 3
- 2021Label Assistant: A Workflow for Assisted Data Annotation in Image Segmentation Taskscited by 1
- 2022EasyMLServe: Easy Deployment of REST Machine Learning Servicescited by 1
- 2021Smart Data Representations: Impact on the Accuracy of Deep Neural Networkscited by 0
- 2022AutoPV: Automated photovoltaic forecasts with limited information using an ensemble of pre-trained modelscited by 0
- 2023Creating Probabilistic Forecasts from Arbitrary Deterministic Forecasts using Conditional Invertible Neural Networkscited by 0
- 2023ProbPNN: Enhancing Deep Probabilistic Forecasting with Statistical Informationcited by 0
- 2023ExeKGLib: Knowledge Graphs-Empowered Machine Learning Analytics4 May 2023
- 2023ProbPNN: Enhancing Deep Probabilistic Forecasting with Statistical Information6 Feb 2023
- 2023Creating Probabilistic Forecasts from Arbitrary Deterministic Forecasts using Conditional Invertible Neural Networks3 Feb 2023
- 2022AutoPV: Automated photovoltaic forecasts with limited information using an ensemble of pre-trained models13 Dec 2022
- 2022EasyMLServe: Easy Deployment of REST Machine Learning Services26 Nov 2022
- 2022Review of automated time series forecasting pipelines3 Feb 2022
- 2021Label Assistant: A Workflow for Assisted Data Annotation in Image Segmentation Tasks27 Nov 2021
- 2021Smart Data Representations: Impact on the Accuracy of Deep Neural Networks17 Nov 2021
- 2021Concepts for Automated Machine Learning in Smart Grid Applications26 Oct 2021