A systematic review of Python packages for time series analysis

TL;DR

This systematic review analyzes 40 Python packages for time series analysis, highlighting their features, development aspects, and common practices to aid researchers and practitioners in selecting appropriate tools.

Contribution

The paper provides a comprehensive classification and evaluation of Python time series packages, including their functionalities, documentation, and community characteristics.

Findings

Forecasting is the most common analysis task.

Half of the packages offer access to datasets or synthetic data.

Most packages depend on numpy, scipy, and pandas.

Abstract

This paper presents a systematic review of Python packages with a focus on time series analysis. The objective is to provide (1) an overview of the different time series analysis tasks and preprocessing methods implemented, and (2) an overview of the development characteristics of the packages (e.g., documentation, dependencies, and community size). This review is based on a search of literature databases as well as GitHub repositories. Following the filtering process, 40 packages were analyzed. We classified the packages according to the analysis tasks implemented, the methods related to data preparation, and the means for evaluating the results produced (methods and access to evaluation data). We also reviewed documentation aspects, the licenses, the size of the packages' community, and the dependencies used. Among other things, our results show that forecasting is by far the most frequently implemented task, that half of the packages provide access to real datasets or allow generating synthetic data, and that many packages depend on a few libraries (the most used ones being numpy, scipy and pandas). We hope that this review can help practitioners and researchers navigate the space of Python packages dedicated to time series analysis. We will provide an updated list of the reviewed packages online at https://siebert-julien.github.io/time-series-analysis-python/.