STAD: Joint Spatial-Temporal Dimension and Channel Correlation for Time Series Anomaly Detection

Accurately identifying real anomalies and pseudo-anomalies in complex multi-dimensional time series data has been a difficult problem in time series anomaly detection. To solve this problem, this paper proposes a new framework, STAD, that joint temporal and spatial dimensions. This framework guides the model to capture the correlation information between channels It also aims to learn the deep feature representation of sequences by mining potential information in the spatialtemporal dimension. It can effectively distinguish between true and false anomalies by comparing information from spatial and temporal dimensions. STAD identifies and integrates correlated channels by using a correlation aggregation mechanism to join multiple channels and detect anomalies.In addition, the KAN mixer designed in this paper can effectively extract features from different spatial locations in the spatial-temporal dimension. Through extensive experiments on several public datasets, STAD demonstrates its superiority in terms of accuracy and robustness.