Time series anomaly detection plays a vital role in the supervision of complex systems, including spacecraft operations, industrial production lines, and Internet of Things infrastructures. However, the existing methods face two key challenges: (1) fixed-threshold frequency filters fail to adapt to non-stationary noise, often leading to the loss of critical anomaly signals; and (2) deep models struggle to balance local feature extraction and global temporal dependency, resulting in limited robustness and generalization. To address these problems, we propose the Spectral-Convolutional Anomaly Transformer (SCAT), a unified framework integrating spectral domain adaptive filtering and spatio-temporal gated learning. Specifically, the Spectral Energy Gating Unit (SEGU) dynamically suppresses noise through learnable frequency masking, while Spatio-Temporal Gated Fusion (ST-Gate) combines multi-scale causal convolution and ConvGRU to harmonize local and long-term patterns. A joint optimization strategy further enhances the discrimination between normal and anomalous sequences. Our experiments on five public benchmarks (SMAP, MSL, PSM, SMD, SWaT) showed that SCAT attained an average improvement of 2.46 percentage points on the F1-score relative to leading baseline approaches, demonstrating strong adaptability and robustness in complex noisy environments.
Zhang et al. (2026) studied this question.