论文标题
时间序列建模和异常检测的深基线网络
Deep Baseline Network for Time Series Modeling and Anomaly Detection
论文作者
论文摘要
近年来，深度学习的时间序列增加了。对于时间序列的异常检测方案，例如财务，物联网，数据中心操作等，时间序列通常会根据各种外部因素显示出非常灵活的基线。异常通过躺在远离基线的情况下揭示自己。但是，由于一些挑战，包括基线变化，缺乏标签，噪声干扰，流媒体数据中的实时检测，可解释性等。在本文中，我们开发了一种新颖的深度体系结构来正确提取时间序列的基线，并不总是容易的。通过使用此深层网络，我们可以轻松地找到基线位置，然后提供可靠且可解释的异常检测结果。对综合和公共现实世界数据集的经验评估表明，我们纯粹的无监督算法与最新方法相比，实现了卓越的性能，并且具有良好的实际应用。
Deep learning has seen increasing applications in time series in recent years. For time series anomaly detection scenarios, such as in finance, Internet of Things, data center operations, etc., time series usually show very flexible baselines depending on various external factors. Anomalies unveil themselves by lying far away from the baseline. However, the detection is not always easy due to some challenges including baseline shifting, lacking of labels, noise interference, real time detection in streaming data, result interpretability, etc. In this paper, we develop a novel deep architecture to properly extract the baseline from time series, namely Deep Baseline Network (DBLN). By using this deep network, we can easily locate the baseline position and then provide reliable and interpretable anomaly detection result. Empirical evaluation on both synthetic and public real-world datasets shows that our purely unsupervised algorithm achieves superior performance compared with state-of-art methods and has good practical applications.
