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

A Review of Open Source Software Tools for Time Series Analysis