动态调整隐状态更新，实现高速传感器异常检测
Adaptive State-Space Mamba for Real-Time Sensor Data Anomaly Detection
- 引入自适应门控机制，根据上下文和统计特征调节隐状态
- 在真实与合成数据集上均优于现有基线方法
- 适合需要快速可靠检测的实时时序任务
状态空间建模已成为自然语言处理、时间序列预测和信号处理等任务中序列分析的强大范式。本文提出一种用于实时传感器数据异常检测的自适应状态空间马姆巴（Adaptive State-Space Mamba, ASSM）框架。尽管状态空间模型曾被用于图像处理（如风格迁移），本工作将其核心思想——序列隐状态——应用于一个截然不同的领域：流式传感器数据的异常检测。具体而言，我们设计了一种自适应门控机制，根据上下文信息和学习到的统计特征动态调节隐状态更新，确保模型在高数据到达率下仍保持计算高效与可扩展性。在真实世界与合成传感器数据集上的大量实验表明，该方法在异常检测性能上显著优于现有基线。所提方法易于扩展至其他对快速可靠检测有要求的时序任务。
原文摘要 · Abstract (English)
State-space modeling has emerged as a powerful paradigm for sequence analysis in various tasks such as natural language processing, time-series forecasting, and signal processing. In this work, we propose an \emph{Adaptive State-Space Mamba} (\textbf{ASSM}) framework for real-time sensor data anomaly detection. While state-space models have been previously employed for image processing applications (e.g., style transfer \cite{wang2024stylemamba}), our approach leverages the core idea of sequential hidden states to tackle a significantly different domain: detecting anomalies on streaming sensor data. In particular, we introduce an adaptive gating mechanism that dynamically modulates the hidden state update based on contextual and learned statistical cues. This design ensures that our model remains computationally efficient and scalable, even under rapid data arrival rates. Extensive experiments on real-world and synthetic sensor datasets demonstrate that our method achieves superior detection performance compared to existing baselines. Our approach is easily extensible to other time-series tasks that demand rapid and reliable detection capabilities.
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