###### Abstract  
Multivariate time series anomaly detection (MTSAD) can effectively identify and analyze anomalous behavior in complex systems, which is particularly important in fields such as financial monitoring, industrial equipment fault detection, and cybersecurity. MTSAD requires simultaneously analyze temporal dependencies and inter-variable relationships have prompted researchers to develop specialized deep learning models to detect anomalous patterns. In this paper, we conducted a structured and comprehensive overview of the latest techniques in deep learning for multivariate time series anomaly detection methods. Firstly, we proposed a taxonomy for the anomaly detection strategies from the perspectives of learning paradigms and deep learning models, and then provide a systematic review that emphasizes their advantages and drawbacks. We also organized the public datasets for time series anomaly detection along with their respective application domains. Finally, open issues for future research on MTSAD were identified. anomaly detection; deep learning network; multivariate time series 2025  
which are used to model and predict time series data. Classical machine learning methods include One-Class Support Vector Machine (OCSVM) [9] and Support Vector Data Description (SVDD) [10]. SVDD is similar to OCSVM, but instead of finding just a boundary, it creates a hypersphere that contains the normal data. Any data points outside this hypersphere are considered anomalies. Distance-based techniques assess the deviation of observations from representative data points using distance metrics, while distributional methods focus on identifying anomalies through points with low likelihood. Density-based methods are based on the local density of data points. If a data point has significantly lower local density compared to its neighboring area, it may be flagged as an anomaly. However, as target systems grow in size and complexity, these methods encounter challenges, particularly their limitations in handling multidimensional data and the lack of labeled anomalies [11]. The challenge of MTSAD lies in the need to consider both the dynamic changes along the temporal dimension and the interrelationships between observations simultaneously. Recently, deep learning-based techniques have advanced the field of anomaly detection within multi-dimensional datasets. These methods exploit powerful deep learning models like Transformers [12; 13; 14], GNN [15], VAEs [16], GANs [17], Diffusion [18] etc., are adept at representing intricate, non-linear relationships among various sensors and are proficient in capturing temporal correlations and dependencies effectively [19]. These advanced models leverage the capabilities of deep learning to identify and learn subtle patterns in data, enabling accurate identification and early warning of anomalous behaviors across various fields such as financial transaction monitoring, cybersecurity threat detection, industrial equipment maintenance forecasting, and healthcare monitoring. The progress of these technologies allows us to better understand and analyze the complex dynamics within multi-dimensional datasets, providing powerful tools for real-time anomaly detection and decision support systems. In this paper, we aim to address the current knowledge gap by exploring features of time series, types of anomalies, and offering a thorough overview of recent advancements in deep learning techniques for MTSAD. It reviews the latest deep learning methods for this purpose from three perspectives and offers a comprehensive discussion about the classification of deep learning models. Additionally, it compiles and organizes public datasets for time series anomaly detection, along with their respective application domains. Finally, the paper wraps up by highlighting possible directions for future research to further develop the field.