用于实时传感器数据异常检测的自适应状态空间Mamba
Adaptive State-Space Mamba for Real-Time Sensor Data Anomaly Detection
AI总结：
本研究提出自适应状态空间Mamba（ASSM）框架，通过自适应门控机制动态调节隐状态更新，实现流式传感器数据的实时异常检测，在多数据集上性能优于现有基线，且可扩展至其他时间序列任务。
摘要：
状态空间建模已成为自然语言处理、时间序列预测、信号处理等各类任务中序列分析的强大范式。在本研究中，我们提出了一种用于实时传感器数据异常检测的自适应状态空间Mamba（Adaptive State-Space Mamba，简称ASSM）框架。尽管状态空间模型此前已被应用于图像处理任务（例如风格迁移StyleMamba），我们的方法利用序列隐状态的核心思想来应对一个截然不同的领域：流式传感器数据的异常检测。\n具体而言，我们引入了一种自适应门控机制，该机制基于上下文和学习到的统计线索动态调节隐状态更新。这一设计确保了即使在数据到达速率很高的情况下，我们的模型仍能保持计算高效性和可扩展性。在真实世界和合成传感器数据集上的大量实验表明，与现有基线方法相比，我们的方法取得了更优的检测性能。我们的方法可轻松扩展到其他需要快速可靠检测能力的时间序列任务。
英文摘要：
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.
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。
