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：近期数字化的进步使得多变量时间序列数据在各种领域得以应用，从而实现了对操作的实时监控。在这些场景中，识别异常数据模式并检测潜在故障十分重要，但同时也颇具挑战。本文提出了一种新颖的无监督时间序列异常检测方法。所提出的框架联合学习观测模型与动态模型，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网络的编码器-解码器来表示观测空间与潜在空间之间的映射。通过利用反向与前向时间信息，同时对状态进行双向转换建模。潜在空间的正则化对正常样本的状态施加约束，并采用马氏距离评估异常程度。在合成数据集与现实世界数据集上的实证研究表明，所提出方法在异常检测任务中具有优越性能。