Unified Taxonomy for Multivariate Time Series Anomaly Detection using Deep Learning

TL;DR

This paper introduces a comprehensive taxonomy for deep learning-based multivariate time series anomaly detection, categorizing methods across eleven dimensions to unify and guide future research in the rapidly evolving field.

Contribution

It presents a novel, validated taxonomy with eleven dimensions that systematizes existing methods and accommodates future developments in MTSAD research.

Findings

Convergence towards Transformer-based models in MTSAD

Reconstruction and prediction models dominate current approaches

Taxonomy captures methodological trends and guides future research

Abstract

The topic of Multivariate Time Series Anomaly Detection (MTSAD) has grown rapidly over the past years, with a steady rise in publications and Deep Learning (DL) models becoming the dominant paradigm. To address the lack of systematization in the field, this study introduces a novel and unified taxonomy with eleven dimensions over three parts (Input, Output and Model) for the categorization of DL-based MTSAD methods. The dimensions were established in a two-fold approach. First, they derived from a comprehensive analysis of methodological studies. Second, insights from review papers were incorporated. Furthermore, the proposed taxonomy was validated using an additional set of recent publications, providing a clear overview of methodological trends in MTSAD. Results reveal a convergence toward Transformer-based and reconstruction and prediction models, setting the foundation for emerging adaptive and generative trends. Building on and complementing existing surveys, this unified taxonomy is designed to accommodate future developments, allowing for new categories or dimensions to be added as the field progresses. This work thus consolidates fragmented knowledge in the field and provides a reference point for future research in MTSAD.