STAR: Boosting Time Series Foundation Models for Anomaly Detection Through State-Aware Adapter

Existing Time Series Foundation Models (TSFMs) for Multivariate Time Series Anomaly Detection often overlook discrete state variables that describe system status and treat them uniformly with numerical variables. This inappropriate modeling approach prevents the model from fully leveraging state information and even leads to significant performance degradation when state variables are integrated. To address this limitation, this paper proposes a novel STate-aware AdapteR (STAR). Specifically, STAR comprises three core innovative components: (1) an Identity-guided State Encoder that effectively captures the complex semantics of state variables; (2) a Conditional Bottleneck Adapter that dynamically injects state influence into TSFMs; and (3) a Numeral-State Matching module that effectively detects anomalies inherent to the state variables themselves. Extensive experiments on real-world datasets demonstrate that STAR significantly improves the performance of existing TSFMs.