A Review of AutoML Software Tools for Time Series Forecasting and Anomaly Detection

Christian O’Leary, Farshad Ghassemi Toosi, Conor Lynch

2023

Abstract

Time series exist across a plethora of domains such as sensors, market prices, network traffic, and health monitoring. Modelling time series data allows researchers to perform trend analysis, forecasting, anomaly detection, predictive maintenance, and data exploration. Given the theoretical and technical knowledge required to implement mathematical and machine learning models, numerous software libraries have emerged to facilitate the programming of these algorithms via automated machine learning (AutoML). Comparatively few studies compare such technologies in the context of time series analysis and existing tools are often limited in functionality. This review paper presents an overview of AutoML software for time series data for both forecasting and anomaly detection. The analysis considers 28 metrics that indicate functionality coverage, code maturity, and community support across 22 AutoML libraries. These aspects of software development are crucial for the uptake and utilisation of AutoML tools. This study proposes a means of deriving a functionality score for correlation analysis between variables such as lines of code, package downloads from PyPi, and GitHub issue completion rate. This review paper also presents an overview of AutoML library features which can facilitate informed decisions on which tools are most appropriate in various instances.

Paper Citation

in Harvard Style

O’Leary C., Ghassemi Toosi F. and Lynch C. (2023). A Review of AutoML Software Tools for Time Series Forecasting and Anomaly Detection. In Proceedings of the 15th International Conference on Agents and Artificial Intelligence - Volume 3: ICAART, ISBN 978-989-758-623-1, pages 421-433. DOI: 10.5220/0011683000003393

in Bibtex Style

@conference{icaart23,
author={Christian O’Leary and Farshad Ghassemi Toosi and Conor Lynch},
title={A Review of AutoML Software Tools for Time Series Forecasting and Anomaly Detection},
booktitle={Proceedings of the 15th International Conference on Agents and Artificial Intelligence - Volume 3: ICAART,},
year={2023},
pages={421-433},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0011683000003393},
isbn={978-989-758-623-1},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 15th International Conference on Agents and Artificial Intelligence - Volume 3: ICAART,
TI - A Review of AutoML Software Tools for Time Series Forecasting and Anomaly Detection
SN - 978-989-758-623-1
AU - O’Leary C.
AU - Ghassemi Toosi F.
AU - Lynch C.
PY - 2023
SP - 421
EP - 433
DO - 10.5220/0011683000003393