A Review of Open Source Software Tools for Time Series Analysis

03/10/2022
by   Yunus Parvej Faniband, et al.
King Fahd University of Petroleum & Minerals
Universiti Putra Malaysia
0

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.

READ FULL TEXT

page 1

page 2

page 3

page 4

01/09/2018

Greenhouse: A Zero-Positive Machine Learning System for Time-Series Anomaly Detection

This short paper describes our ongoing research on Greenhouse - a zero-p...
06/18/2021

pyWATTS: Python Workflow Automation Tool for Time Series

Time series data are fundamental for a variety of applications, ranging ...
09/20/2021

Merlion: A Machine Learning Library for Time Series

We introduce Merlion, an open-source machine learning library for time s...
07/26/2023

Unraveling the Complexity of Splitting Sequential Data: Tackling Challenges in Video and Time Series Analysis

Splitting of sequential data, such as videos and time series, is an esse...
02/08/2022

KENN: Enhancing Deep Neural Networks by Leveraging Knowledge for Time Series Forecasting

End-to-end data-driven machine learning methods often have exuberant req...
03/21/2022

Forecast Evaluation for Data Scientists: Common Pitfalls and Best Practices

Machine Learning (ML) and Deep Learning (DL) methods are increasingly re...
07/06/2023

FITS: Modeling Time Series with 10k Parameters

In this paper, we introduce FITS, a lightweight yet powerful model for t...

Please sign up or login with your details

Forgot password? Click here to reset