To address the challenges of complex temporal dependencies, scarcity of anomalous samples, and the underutilization of frequency-domain information from time-series data in existing models for multivariate time series anomaly detection, this paper proposes a multivariate time series anomaly detection model based on multi-view feature contrastive learning. The model constructs dual feature channels by learning representations of both time-domain and frequency-domain information, and employs a pure contrastive loss to guide the learning process. Additionally, a block-based strategy and graph attention mechanism are adopted in the design of the time-domain channel, while the analysis of temporal variations is extended to a two-dimensional space in the frequency-domain channel, utilizing a multi-scale convolutional module to further enhance the representational capacity of time series data, thereby improving anomaly detection accuracy. Experiments on five publicly available multivariate time series datasets demonstrate that the proposed model achieves superior performance in multidimensional time series anomaly detection tasks.
