SPECGATE: CROSS-DOMAIN CONTRASTIVE PROTO TYPES WITH SPECTRAL GATING FOR UNSUPERVISED TIME-SERIES ANOMALY DETEC TION

Unsupervised anomaly detection on multivariate time series is dominated by reconstruction- and density-based models, yet most treat the temporal and spec tral views in isolation or rely on a single contrastive pretext that can collapse in narrow industrial regimes. We propose SPECGATE, a model that brings three complementary signals to the same point in time. A shared patch transformer learns a time-domain view through masked reconstruction; a learned frequency band gate produces a channel-adaptive spectral view whose reconstruction error grows when energy appears outside the normal spectral support; an InfoNCE ob jective aligns the two views in the joint space; and a normalised prototype mem ory banks the normal manifold so that off-manifold anomalies incur large resid ual distance. The three evidence channels are fused with channel-wise standard ised scores. SPECGATE reaches competitive PA-F1 on six standard benchmarks (SMD, MSL, SMAP, PSM, SWaT, WADI) without any anomaly labels at train ing time and with < 2M parameters. Beyond empirical gains, we provide a short analysis showing that the gate acts as a data-driven band-selector and that the prototype memory yields a quantifiable separation between normal and abnormal embeddings under standard concentration assumptions.