# pyWATTS: Python Workflow Automation Tool for Time Series

arXiv: https://arxiv.org/abs/2106.10157 · HTML: https://mlsanity.com/paper/2106.10157/

## 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 (most cited first, 3 of 3)

- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [River: machine learning for streaming data in Python](https://mlsanity.com/paper/2012.04740/) (2020)
- [Data-Driven Copy-Paste Imputation for Energy Time Series](https://mlsanity.com/paper/2101.01423/) (2021)

## Built on by (most cited first, 9 of 9)

- [Review of automated time series forecasting pipelines](https://mlsanity.com/paper/2202.01712/) (2022)
- [Concepts for Automated Machine Learning in Smart Grid Applications](https://mlsanity.com/paper/2110.13585/) (2021)
- [ExeKGLib: Knowledge Graphs-Empowered Machine Learning Analytics](https://mlsanity.com/paper/2305.02966/) (2023)
- [Label Assistant: A Workflow for Assisted Data Annotation in Image Segmentation Tasks](https://mlsanity.com/paper/2111.13970/) (2021)
- [EasyMLServe: Easy Deployment of REST Machine Learning Services](https://mlsanity.com/paper/2211.14417/) (2022)
- [Smart Data Representations: Impact on the Accuracy of Deep Neural Networks](https://mlsanity.com/paper/2111.09128/) (2021)
- [AutoPV: Automated photovoltaic forecasts with limited information using an ensemble of pre-trained models](https://mlsanity.com/paper/2212.06797/) (2022)
- [Creating Probabilistic Forecasts from Arbitrary Deterministic Forecasts using Conditional Invertible Neural Networks](https://mlsanity.com/paper/2302.01800/) (2023)
- [ProbPNN: Enhancing Deep Probabilistic Forecasting with Statistical Information](https://mlsanity.com/paper/2302.02597/) (2023)

## 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.