A representation learning perspective on revisiting reconstruction-based time-series anomaly detection

Reconstruction-based approaches are one of the mainstream paradigms for time- series anomaly detection. Existing studies typically improve anomaly detection performance by designing sophisticated network architectures or introducing prior biases to capture specific characteristics of time-series data. However, we find that these approaches still have limitations in effectively capturing data characteristics and often incur substantial computational costs. To address these issues, we in- troduce a Transformer-based patch-level anomaly detection model that focuses on learning global representations of time-series data. By combining point-wise and interval-wise anomaly scores, our method achieves anomaly detection perfor- mance comparable to or even better than that of mainstream approaches, without requiring complex network architectures or manually designed prior biases. It also achieves a favorable trade-off between detection performance and computational cost. Furthermore, we investigate the scalability and transferability of our approach, providing a new perspective on reconstruction-based time-series anomaly detec- tion and suggesting that increasingly complex network architectures may not be necessary for effective anomaly detection