  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.
