用强化学习+自编码器+主动学习，自动识别时间序列新异常。
Anomaly Detection in Time Series Data Using Reinforcement Learning, Variational Autoencoder, and Active Learning
- 结合DRL、VAE与主动学习，自动建模时间序列依赖关系。
- 仅需少量标注数据即可检测新型异常，提升识别效率。
- 适合金融、工业传感器等需要快速响应异常的场景。
本文提出一种新颖的时间序列异常检测方法，适用于数据中心、传感器网络和金融等领域。传统方法常面临参数调优困难且难以适应新异常类型的问题。本方法通过融合深度强化学习（DRL）、变分自编码器（VAE）与主动学习，利用长短期记忆网络（LSTM）有效建模序列数据及其依赖关系，实现对新型异常类别的低标注数据检测。在真实数据集上的评估表明，该DRL-VAE与主动学习的结合显著优于现有方法，推动了时间序列分析技术的发展。
原文摘要 · Abstract (English)
A novel approach to detecting anomalies in time series data is presented in this paper. This approach is pivotal in domains such as data centers, sensor networks, and finance. Traditional methods often struggle with manual parameter tuning and cannot adapt to new anomaly types. Our method overcomes these limitations by integrating Deep Reinforcement Learning (DRL) with a Variational Autoencoder (VAE) and Active Learning. By incorporating a Long Short-Term Memory (LSTM) network, our approach models sequential data and its dependencies effectively, allowing for the detection of new anomaly classes with minimal labeled data. Our innovative DRL- VAE and Active Learning combination significantly improves existing methods, as shown by our evaluations on real-world datasets, enhancing anomaly detection techniques and advancing time series analysis.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。