Dive into Time-Series Anomaly Detection: A Decade Review

TL;DR

This paper provides a comprehensive review of a decade of research in time-series anomaly detection, categorizing methods and analyzing trends to guide future developments in this vital field.

Contribution

It introduces a novel process-centric taxonomy for classifying time-series anomaly detection methods and offers a meta-analysis of research trends over the past decade.

Findings

Identification of key research trends and shifts in methodologies

Development of a structured taxonomy for anomaly detection methods

Insights into the evolution of machine learning applications in the field

Abstract

Recent advances in data collection technology, accompanied by the ever-rising volume and velocity of streaming data, underscore the vital need for time series analytics. In this regard, time-series anomaly detection has been an important activity, entailing various applications in fields such as cyber security, financial markets, law enforcement, and health care. While traditional literature on anomaly detection is centered on statistical measures, the increasing number of machine learning algorithms in recent years call for a structured, general characterization of the research methods for time-series anomaly detection. This survey groups and summarizes anomaly detection existing solutions under a process-centric taxonomy in the time series context. In addition to giving an original categorization of anomaly detection methods, we also perform a meta-analysis of the literature and outline general trends in time-series anomaly detection research.