STAR：通过状态感知适配器增强用于异常检测的时间序列基础模型
STAR: Boosting Time Series Foundation Models for Anomaly Detection through State-aware Adapter
AI总结：
本文提出即插即用的状态感知适配器STAR，通过状态编码、条件低秩适配和数值-状态匹配，增强TSFM对离散状态变量的利用并提升MTSAD性能。
摘要：
尽管时间序列基础模型（TSFM）在多变量时间序列异常检测（MTSAD）中已取得显著成功，但在真实工业场景中，许多时间序列不仅包含温度、流量等数值变量，还包含大量描述系统状态的离散状态变量，例如阀门开/关或星期几。现有TSFM往往忽视状态变量独特的类别属性及其作为条件的关键作用，通常将其与数值变量统一处理。这种不当建模方式使模型无法充分利用状态信息，甚至在纳入状态变量后导致检测性能显著下降。为解决这一关键局限，本文提出一种新颖的状态感知适配器（STate-aware AdapteR，STAR）。STAR是一个即插即用模块，旨在微调阶段增强TSFM建模和利用状态变量的能力。具体而言，STAR包含三个核心组件：（1）设计身份引导状态编码器，通过可学习的State Memory有效捕获状态变量复杂的类别语义；（2）提出条件瓶颈适配器，根据当前状态动态生成低秩适配参数，从而将状态变量的影响灵活注入骨干模型；（3）引入数值-状态匹配模块，以更有效地检测状态变量自身固有的异常。在真实数据集上开展的大量实验表明，STAR能够提升现有TSFM在MTSAD上的性能。
英文摘要：
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 STate-aware AdapteR (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 comprisesthree core components: (1) We design an Identity-guided State Encoder, whicheffectively 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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
