用强化学习提升多变量时间序列异常检测准确率
Calibrated Unsupervised Anomaly Detection in Multivariate Time-series using Reinforcement Learning
- 在自编码器隐空间中用强化学习探索异常模式
- 通过合成异常数据校准决策边界，降低漏报率
- 结合小波分析捕捉多尺度异常，适合工业监控场景
本文研究基于强化学习（RL）的多变量时间序列无监督异常检测方法。由于异常数据稀缺，模型常将异常误判为正常，导致高漏报率。通过在自编码器的隐空间中引入强化学习，促进训练过程中的探索与利用平衡，有效避免过拟合。同时，采用小波分析将时间序列分解至时频域，提取小波系数以捕捉突发性与渐进性异常变化，实现多分辨率检测。通过生成合成异常数据，并嵌入监督机制来校准决策边界，进一步优化模型对正常与异常模式的区分能力。
原文摘要 · Abstract (English)
This paper investigates unsupervised anomaly detection in multivariate time-series data using reinforcement learning (RL) in the latent space of an autoencoder. A significant challenge is the limited availability of anomalous data, often leading to misclassifying anomalies as normal events, thus raising false negatives. RL can help overcome this limitation by promoting exploration and balancing exploitation during training, effectively preventing overfitting. Wavelet analysis is also utilized to enhance anomaly detection, enabling time-series data decomposition into both time and frequency domains. This approach captures anomalies at multiple resolutions, with wavelet coefficients extracted to detect both sudden and subtle shifts in the data, thereby refining the anomaly detection process. We calibrate the decision boundary by generating synthetic anomalies and embedding a supervised framework within the model. This supervised element aids the unsupervised learning process by fine-tuning the decision boundary and increasing the model's capacity to distinguish between normal and anomalous patterns effectively.
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