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：多变量时间序列异常检测（MTSAD）领域在过去数年中迅速发展，相关论文数量稳步增长，深度学习（DL）模型已成为主导范式。为解决该领域缺乏系统化分类的问题，本研究提出一种新颖且统一的分类体系，包含十一个维度，涵盖三个部分（输入、输出和模型），用于对基于深度学习的MTSAD方法进行分类。这些维度通过双重途径确立：首先，基于对方法论研究的全面分析推导得出；其次，融合了综述论文的见解。进一步地，采用一组近期发表的论文对所提出分类体系进行验证，清晰展示了MTSAD方法学的发展趋势。研究结果表明，基于Transformer的重建与预测模型趋于收敛，为新兴的自适应与生成式趋势奠定基础。本统一分类体系在现有综述基础上进行构建与补充，旨在适应未来研究进展，允许随着领域发展新增类别或维度。本研究因此整合了该领域分散的知识，为MTSAD未来研究提供了参考基准。