Abstract
We propose two solutions to outlier detection in
time series based on recurrent autoencoder ensembles. The solutions exploit autoencoders built using sparsely-connected recurrent neural networks
(S-RNNs). Such networks make it possible to generate multiple autoencoders with different neural
network connection structures. The two solutions
are ensemble frameworks, specifically an independent framework and a shared framework, both of
which combine multiple S-RNN based autoencoders
to enable outlier detection. This ensemble-based
approach aims to reduce the effects of some autoencoders being overfitted to outliers, this way improving overall detection quality. Experiments with
two real-world time series data sets, including univariate and multivariate time series, offer insight
into the design properties of the proposed ensemble frameworks and demonstrate that the proposed
frameworks are capable of outperforming both baselines and the state-of-the-art methods