Do We Really Need Complex Models? Revisiting PCA for Time-Series Anomaly Detection

Time-series anomaly detection increasingly relies on complex deep or foundation-model architectures, while practical deployment demands methods that are accurate, efficient, and scalable. PCA offers an efficient and interpretable model of normal variation, but its standard formulation operates on static vector representations without explicitly accounting for temporal structure. We introduce TAP, a Time-series Adaptive PCA framework that addresses this limitation through three novel designs: Multi-Resolution PCA captures anomalies across different durations and temporal resolutions; Cross-Resolution Evidence Consensus identifies anomaly evidence supported by related representations; and Shape-Residual PCA detects deviations from recurrent normal patterns. We further develop efficient covariance compression and batched alignment to support scalable computation. Extensive experiments across 23 public dataset collections demonstrate state-of-the-art overall detection performance, improving upon the second-best method by 17.9% while running 10 times faster. On a single CPU core, TAP requires only 2.5 seconds per benchmark series on average and processes approximately 28,000 points per second, making it practical for accurate, large-scale monitoring and diagnostic workloads. The code is available at https://anonymous.4open.science/r/TAP-TSAD.