Unsupervised Dual-Domain Memory Model for Time Series Anomaly Detection

The continuous advancement of multimedia technology has led to the exponential accumulation of massive time-stamped data. However, accurately identifying anomalies in such data remains a major challenge. Current anomaly detection methods still face serious limitations, including the difficulty in handling complex time series data and the anomaly masking phenomenon caused by overlapping temporal patterns. Existing methods cannot effectively address these challenges. To overcome these limitations, we propose an unsupervised time series anomaly detection algorithm DMemAD based on a dual-domain memory module. Specifically, we design an STD Mamba structure that can effectively extract trend and seasonal components in the series and enhance the connection between elements in each component through bidirectional learning. Second, we design a dual-domain memory module to avoid anomaly masking by independently storing trend and seasonal patterns. Additionally, we propose a residual-based memory update mechanism to enhance the accuracy of memory updates, ensuring that prototype patterns are stored precisely. Extensive experiments on four datasets from different domains show that DMemAD achieves an average F1 score of 96.81%, outperforming 17 baseline methods and establishing state-of-the-art performance.