Multivariate Time Series Anomaly Detection With Hierarchical Component-Aware

Multivariate time series anomaly detection (MTSAD) remains challenging due to the complexity of spatiotemporal dependencies, non-stationary dynamics, and heterogeneous variable interactions. Existing methods often struggle to simultaneously model correlations within and across different time series components, limiting their ability to capture hierarchical patterns at multiple scales. To address these issues, we propose HCAAD (Hierarchical Component-Aware multivariate time series Anomaly Detection), an unsupervised framework that combines frequency-adaptive multiscale decomposition with cross-component correlation modeling. First, we use a Fast Fourier Transform (FFT)-based decomposition to split the time series into multiple components. This step isolates long-term trends, seasonal cycles, and transient fluctuations. Second, we design a dynamic correlation matrix to explicitly model intra-and inter-component dependencies. An attention mechanism further refines these correlations by adaptively integrating global spatiotemporal patterns. Experiments on six benchmark datasets show that HCAAD consistently achieves state-of-the-art performance.