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.

翻译：多元时间序列异常检测（MTSAD）领域近年来发展迅速，相关出版物数量稳步增长，深度学习（DL）模型已成为主导范式。针对该领域缺乏系统化归纳的现状，本研究提出了一种新颖且统一的分类体系，包含输入、输出和模型三个部分的十一个维度，用于对基于深度学习的MTSAD方法进行分类。这些维度通过双重方法确立：首先，基于对方法论研究的全面分析推导得出；其次，融入了综述论文的见解。此外，利用一组近期出版物对所提出的分类体系进行了验证，清晰展示了MTSAD方法论趋势。结果表明，模型正趋向于基于Transformer的重建与预测模型，这为新兴的自适应与生成趋势奠定了基础。本项统一分类体系在现有综述的基础上进行构建和补充，旨在适应未来发展，允许随着领域进展添加新的类别或维度。因此，本工作整合了该领域零散的知识，并为MTSAD的未来研究提供了参考基点。