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 also provide an updated list of the reviewed packages online.

This paper presents a systematic review of Python packages focused on time series analysis. The objective is first to provide an overview of the different time series analysis tasks and preprocessing methods implemented, but also to give an overview of the development characteristics of the packages (e.g., dependencies, community size, etc.). This review is based on a search of literature databases as well as GitHub repositories. After 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 to evaluate the results produced (methods and access to evaluation data). We also reviewed the licenses, the packages community size, and the dependencies used. Among other things, our results show that forecasting is by far the most 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). One of the lessons learned from this review is that the process of finding a given implementation is not inherently simple, and we hope that this review can help practitioners and researchers navigate the space of Python packages dedicated to time series analysis.