Deep Learning for Time Series Anomaly Detection: A Survey

TL;DR

This survey reviews deep learning models for time series anomaly detection, categorizing methods, discussing their advantages and limitations, and highlighting recent applications and open research challenges.

Contribution

It provides a comprehensive taxonomy and analysis of deep learning-based anomaly detection models for time series data, including recent applications and future challenges.

Findings

Deep learning models effectively detect anomalies in complex time series.

Taxonomy categorizes models based on detection factors and techniques.

Open issues include model interpretability and handling diverse data types.

Abstract

Time series anomaly detection has applications in a wide range of research fields and applications, including manufacturing and healthcare. The presence of anomalies can indicate novel or unexpected events, such as production faults, system defects, or heart fluttering, and is therefore of particular interest. The large size and complex patterns of time series have led researchers to develop specialised deep learning models for detecting anomalous patterns. This survey focuses on providing structured and comprehensive state-of-the-art time series anomaly detection models through the use of deep learning. It providing a taxonomy based on the factors that divide anomaly detection models into different categories. Aside from describing the basic anomaly detection technique for each category, the advantages and limitations are also discussed. Furthermore, this study includes examples of deep anomaly detection in time series across various application domains in recent years. It finally summarises open issues in research and challenges faced while adopting deep anomaly detection models.