## 8 Conclusions and Future Direction  
This article provides a comprehensive survey and review of multivariate time series anomaly detection, exploring types of anomalies and deep architectures for anomaly detection. It analyzes and organizes deep learning model architectures for anomaly detection from three perspectives, proposes a new classification of anomaly detection strategies, and discusses 46 deep learning models in depth. In addition, it compiles public datasets for multivariate time series anomaly detection, along with their respective application domains. To advance the field, the article concludes with a discussion of potential directions for future research exploration.  
Discrepancy: The contrastive learning has attracted considerable attention across various domains, as it is capable of training models by contrasting positive pairs with negative pairs. Recent research has been focusing on highlighting discrepancies [59; 62], and further exploration in this direction is expected.  
Integration of domain knowledge: Integrating existing knowledge from the statistical domain with deep learning can further enhance the model's capability for time series anomaly detection. FCVAE [126] selects the most useful information from the frequency domain to achieve better short-term trend construction. Future work could leverage insights from the frequency domain, combined with domain knowledge, to strengthen the analysis of time series data.  
Benchmarking and evaluation metrics: The development and use of diverse public datasets that simulate real-world anomaly situations, with clear subset divisions for training and testing. At the same time, the design of intuitive, interpretable, and parameter-adjustment-free evaluation metrics to adapt to different types of time series anomaly detection.  
Leveraging techniques from LLMs: Recently, LLMs have gained popularity in time series analysis [127]. LLMs exhibit exceptional generalization capabilities, demonstrating robust predictive performance even with scarce datasets. This characteristic is particularly valuable in the context of MTSAD. By leveraging multimodal knowledge, such as incorporating additional textual information to generate multimodal embeddings, LLMs effectively bridge the gap between normal and anomalous data in anomaly detection. This modeling approach enriches the anomaly detection process by providing a more comprehensive data representation, enabling the inclusion of diverse data sources and fostering more detailed and context-aware anomaly detection. Exploring LLMs in MTSAD is a promising avenue that could significantly enhance the accuracy and efficiency of detecting anomalies in multivariate time series data.  
Writing--original draft preparation, F.W.; writing--review and editing, F.W., Y.J., R.Z., A.W. and J.X.; supervision, X.P.; Project Administration, A.W.; Funding Acquisition, J.X. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the 2021 Key Research Platforms and Projects of Colleges and Universities by the Department of Education of Guangdong Province: Research on the Construction of Knowledge Graph Models for Vocational College Architectural Education Supported by Artificial Intelligence (Project No.: 2021 ZDZX1112).  
Not applicable.  
The data presented in this study are available on the source mentioned in the text.  
The authors declare no conflicts of interest.