Recent advances in digitization have led to the availability of multivariate time series data in various domains, enabling real-time monitoring of operations. Identifying abnormal data patterns and detecting potential failures in these scenarios are important yet rather challenging. In this work, we propose a novel unsupervised anomaly detection method for time series data. The proposed framework jointly learns the observation model and the dynamic model, and model uncertainty is estimated from normal samples. Specifically, a long short-term memory (LSTM)-based encoder-decoder is adopted to represent the mapping between the observation space and the latent space. Bidirectional transitions of states are simultaneously modeled by leveraging backward and forward temporal information. Regularization of the latent space places constraints on the states of normal samples, and Mahalanobis distance is used to evaluate the abnormality level. Empirical studies on synthetic and real-world datasets demonstrate the superior performance of the proposed method in anomaly detection tasks.
翻译:摘要: 数字化的最近进展导致各领域多元时间序列数据的可用性,使操作实时监测成为可能。在这些情况下,识别异常数据模式和检测潜在故障非常重要,但也相当具有挑战性。本文提出了一种新颖的时间序列数据无监督异常检测方法。该方法同时学习观测模型和动态模型,并从正常样本中估计模型不确定性。具体而言,采用基于长短期记忆(LSTM)的编码器-解码器来表示观测空间与潜在空间之间的映射。利用向后和向前的时间信息分别建立状态的双向转换。潜在空间的正则化将约束正常样本的状态,并使用马氏距离评估异常级别。对合成和现实世界数据集的实证研究表明,所提出的方法在异常检测任务中具有优越的性能。