关键词: 异常检测, 多尺度特征提取, 时间序列, 状态空间模型, 无监督学习

Abstract: Multivariate time series data often exhibit multi-scale characteristics and complex interdependencies, posing challenges for anomaly detection. To address this issue, an innovative unsupervised multivariate time series anomaly detection model was developed, namely M3AD (Multi-scale Mamba Multi-Layer Perceptron Anomaly Detection). Firstly, a multi-scale feature extraction method was employed, segmenting the time series across different temporal scales to capture both short-term and long-term patterns. Secondly, a MultiLayer Perceptron (MLP) and convolutional layers were utilized for feature learning, extracting local and high-level feature representations. Subsequently, a selective state-space model Mamba was introduced to efficiently capture key information in long sequences. Finally, sensitive anomaly detection was achieved through a loss function based on KL (Kullback-Leibler) divergence and anomaly score calculation. To validate the model's effectiveness, M3AD was compared with seven models, such as AnomalyTransformer and Memto, across four public datasets. Experimental results showed that M3AD demonstrated significant advantages and leading performance compared to existing methods in key metrics such as precision, recall, and F1 score. The results demonstrated the effectiveness and superiority of M3AD in the task of multivariate time series anomaly detection.

Key words: anomaly detection, multi-scale feature extraction, time series, state-space model, unsupervised learning