# A Review of Open Source Software Tools for Time Series Analysis

arXiv: https://arxiv.org/abs/2203.05195 · HTML: https://mlsanity.com/paper/2203.05195/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 18 of its 102 references that are in the map.
- First posted on arXiv 10 Mar 2022.

## Builds on (most cited first, 18 of 18)

- [Detecting Spacecraft Anomalies Using LSTMs and Nonparametric Dynamic Thresholding](https://mlsanity.com/paper/1802.04431/) (2018)
- [Financial Time Series Forecasting with Deep Learning : A Systematic Literature Review: 2005-2019](https://mlsanity.com/paper/1911.13288/) (2019)
- [PyOD: A Python Toolbox for Scalable Outlier Detection](https://mlsanity.com/paper/1901.01588/) (2019)
- [Deep Learning for Sensor-based Activity Recognition: A Survey](https://mlsanity.com/paper/1707.03502/) (2017)
- [sktime: A Unified Interface for Machine Learning with Time Series](https://mlsanity.com/paper/1909.07872/) (2019)
- [River: machine learning for streaming data in Python](https://mlsanity.com/paper/2012.04740/) (2020)
- [Evaluating Real-time Anomaly Detection Algorithms - the Numenta Anomaly Benchmark](https://mlsanity.com/paper/1510.03336/) (2015)
- [Scikit-Multiflow: A Multi-output Streaming Framework](https://mlsanity.com/paper/1807.04662/) (2018)
- [Deep Learning for IoT Big Data and Streaming Analytics: A Survey](https://mlsanity.com/paper/1712.04301/) (2017)
- [GluonTS: Probabilistic Time Series Models in Python](https://mlsanity.com/paper/1906.05264/) (2019)
- [GRATIS: GeneRAting TIme Series with diverse and controllable characteristics](https://mlsanity.com/paper/1903.02787/) (2019)
- [TODS: An Automated Time Series Outlier Detection System](https://mlsanity.com/paper/2009.09822/) (2020)
- [A review on distance based time series classification](https://mlsanity.com/paper/1806.04509/) (2018)
- [MLJ: A Julia package for composable machine learning](https://mlsanity.com/paper/2007.12285/) (2020)
- [PySAD: A Streaming Anomaly Detection Framework in Python](https://mlsanity.com/paper/2009.02572/) (2020)
- [Seglearn: A Python Package for Learning Sequences and Time Series](https://mlsanity.com/paper/1803.08118/) (2018)
- [SUOD: Toward Scalable Unsupervised Outlier Detection](https://mlsanity.com/paper/2002.03222/) (2020)
- [TSML (Time Series Machine Learnng)](https://mlsanity.com/paper/2005.13191/) (2020)

## Abstract (arXiv)

Time series data is used in a wide range of real world applications. In a variety of domains , detailed analysis of time series data (via Forecasting and Anomaly Detection) leads to a better understanding of how events associated with a specific time instance behave. Time Series Analysis (TSA) is commonly performed with plots and traditional models. Machine Learning (ML) approaches , on the other hand , have seen an increase in the state of the art for Forecasting and Anomaly Detection because they provide comparable results when time and data constraints are met. A number of time series toolboxes are available that offer rich interfaces to specific model classes (ARIMA/filters , neural networks) or framework interfaces to isolated time series modelling tasks (forecasting , feature extraction , annotation , classification). Nonetheless , open source machine learning capabilities for time series remain limited , and existing libraries are frequently incompatible with one another. The goal of this paper is to provide a concise and user friendly overview of the most important open source tools for time series analysis. This article examines two related toolboxes (1) forecasting and (2) anomaly detection. This paper describes a typical Time Series Analysis (TSA) framework with an architecture and lists the main features of TSA framework. The tools are categorized based on the criteria of analysis tasks completed , data preparation methods employed , and evaluation methods for results generated. This paper presents quantitative analysis and discusses the current state of actively developed open source Time Series Analysis frameworks. Overall , this article considered 60 time series analysis tools , and 32 of which provided forecasting modules , and 21 packages included anomaly detection.