Title: STAR: Boosting Time Series Foundation Models for Anomaly Detection  Through State-aware Adapter
Primary area: learning on time series and dynamical systems
Keywords: Time Series Anomaly Detection

Abstract: While Time Series Foundation Models (TSFMs) have demonstrated remarkable success in Multivariate Time Series Anomaly Detection (MTSAD), however, in real-world industrial scenarios, many time series comprise not only *numerical variables* such as temperature and flow, but also numerous discrete *state variables* that describe the system status, such as valve on/off or day of the week.
Existing TSFMs often overlook the distinct categorical nature of *state variables* and their critical role as conditions, typically treating them uniformly with *numerical variables*. This inappropriate modeling approach prevents the model from fully leveraging state information and even leads to a significant degradation in detection performance after *state variables* are integrated.

To address this critical limitation, this paper proposes a novel **ST**ate-aware **A**dapte**R** (STAR). STAR is a plug-and-play module designed to enhance the capability of TSFMs in modeling and leveraging *state variables* during the fine-tuning stage. Specifically, STAR comprises three core components: (1) We design an *Identity-guided State Encoder*, which effectively captures the complex categorical semantics of *state variables* through a learnable *State Memory*. (2) We propose a *Conditional Bottleneck Adapter*, which dynamically generates low-rank adaptation parameters conditioned on the current state, thereby flexibly injecting the influence of *state variables* into the backbone model. (3) We also introduce a *Numeral-State Matching* module to more effectively detect anomalies inherent to the state variables themselves. Extensive experiments conducted on real-world datasets demonstrate that STAR can improve the performance of existing TSFMs on MTSAD.