A Deep Autoencoder Based Outlier Detection for Time Series

Time series outlier detection is an important topic in data mining, having significant applications in reality. Due to the complexity and dynamics of time series, it is quite difficult to detect outlier in time series. Particularly, influenced by outside factors, time series are usually unpredictable, accompanied with concept drift. Recently, recurrent neural network has been used to identify time series outlier, and demonstrated great potential. However, RNN usually uses deterministic state transition structure, which cannot characterize the variability of high-dimensional time series. This paper proposes to incorporate latent variables into RNN, aiming to catch the time series variability as much as possible. In particular, our method combines RNN and variation auto-encoder framework. We evaluate our method with several real datasets, and demonstrate that our method has superior detecting performance.