LATF-AD: Learning Locally Adaptive Time-Frequency Normality for Time Series Anomaly Detection

Time series anomaly detection (TSAD) is essential for reliable monitoring of complex systems. Although frequency-aware modeling can reveal latent anomalies, global spectral representations often obscure localized transients, while a single fixed transformation inadequately captures heterogeneous temporal dynamics across varying resolutions. To address these limitations, we propose LATF-AD, a novel framework for locally adaptive time-frequency normality modeling. LATF-AD constructs an ordered bank of heterogeneous time-frequency views and learns content-dependent, locally adaptive routing to accommodate dynamic spectral patterns via a weighted consensus. It further feeds the consensus into dual prediction and reconstruction heads, while incorporating a prototype-guided raw-domain branch to modulate anomaly scoring against time-frequency artifacts. Extensive experiments on the comprehensive TSB-AD Benchmark demonstrate that LATF-AD consistently achieves state-of-the-art performance across both point-wise and range-based metrics.