## 2 Related Work  
In real-world monitoring scenarios, traffic flow data collected by sensors is typically unlabeled [5] and often suffers from incompleteness due to various objective factors, leading to anomalies and missing data. This necessitates anomaly detection models with unsupervised learning capabilities [6; 7]. Additionally, the high dimensionality, dynamic nature, and complexity of MTS data [8] make accurately identifying anomalous data a challenging task. Currently, unsupervised time series anomaly detection methods can be broadly categorized into traditional and deep learning-based approaches. Common traditional methods include statistical approaches (both parametric and non-parametric) as well as reconstruction and clustering techniques based on classical machine learning. In contrast, deep learning-based methods primarily focus on prediction and reconstruction. Statistical methods typically model each variable in MTS data independently, failing to fully leverage the spatial relationships among multiple variables. For highly correlated MTS data, anomaly detection methods based on classical machine learning and deep learning have become the dominant trend in research and application. Reconstruction-based anomaly detection methods for multivariate time series involve training models to learn latent representations of normal time subsequences, reconstructing the multivariate subsequences, and identifying anomalies by measuring the differences between the reconstructed and original sequences. Principal Component Analysis (PCA) is a classical dimensionality reduction and feature extraction technique [9]. It detects anomalies by assessing whether a data sample can be effectively reconstructed. If a sample is difficult to reconstruct, its features are likely inconsistent with the overall dataset, indicating it as an anomaly. [PERSON] et al. [10] applied an improved PCA-based method to detect faults in chiller sensors. [PERSON] et al. [11] proposed an optimization approach that minimizes intra-class reconstruction errors while maximizing inter-class reconstruction errors for training samples. [PERSON] et al. [12] introduced a standardized reconstruction error metric, providing a more effective means for accuracy evaluation. [PERSON] et al. [13] adopted a partially interpretable autoencoder structure to enhance PCA's data compression and reconstruction capabilities. [PERSON] et al. [14] developed a novel semi-supervised anomaly detection framework based on reconstruction similarity. [PERSON] et al. [15] proposed a new anomaly detection framework that integrates statistical analysis with neural network methods. Clustering-based anomaly detection is a typical unsupervised method that distinguishes anomalies based on three common assumptions: data points that do not belong to any cluster are anomalies, data points far from the cluster centers are anomalies, and data points within sparse or small clusters are anomalies. [PERSON] et al. [16] applied K-means clustering to Call Detail Records (CDRs) from both anomaly detection and prediction perspectives, proposing a scalable and efficient anomaly detection method. [PERSON] et al. [17] optimized anomaly detection models by enhancing machine learning classifiers. [PERSON] et al. [18] introduced an anomaly detection algorithm combining clustering techniques with autoencoder models to identify network traffic anomalies. With the widespread application potential of deep learning across various fields, deep learning-based time series anomaly detection methods have garnered significant attention. These methods can learn complex nonlinear temporal relationships and high-dimensional representations of time series data. [PERSON] et al. (2019) proposed a content-based video retrieval (CBVR) framework for anomaly detection in surveillance videos, leveraging deep learning techniques and HNSW (Hierarchical Navigable Small World) indexing. [PERSON] et al.