Recent advances in digitization has led to availability of multivariate time series data in various domains, in order to monitor operations in real time. Identifying abnormal data pattern and detect potential failures in these scenarios are important yet rather difficult tasks. We propose a novel unsupervised anomaly detection method for time series data. Our approach uses sequence encoder and decoder to represent the mapping between time series and hidden state, and learns bidirectional dynamics simultaneously by leveraging backward and forward temporal information in the training process. We further regularize the state space to place constraints on states of normal samples, and use Mahalanobis distance to evaluate abnormality level. Results on synthetic and real-world datasets show the superiority of the proposed method.

翻译：摘要：近年来数字化进程的推进使得各领域能够获取多变量时间序列数据，以实现操作的实时监控。在这些场景中识别异常数据模式并检测潜在故障是重要但相当困难的任务。我们提出一种新颖的无监督时间序列异常检测方法。该方法采用序列编码器与解码器构建时间序列与隐状态之间的映射关系，并在训练过程中通过利用前向和后向时间信息同时学习双向动态特征。进一步地，我们对状态空间进行正则化，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。在合成数据集与真实世界数据集上的实验结果表明了所提方法的优越性。