  Anomaly detection is a classical but worthwhile problem, and many deep
learning-based anomaly detection algorithms have been proposed, which can
usually achieve better detection results than traditional methods. In view of
reconstruct ability of the model and the calculation of anomaly score, this
paper proposes a time series anomaly detection method based on Variational
AutoEncoder model(VAE) with re-Encoder and Latent Constraint network(VELC). In
order to modify reconstruct ability of the model to prevent it from
reconstructing abnormal samples well, we add a constraint network in the latent
space of the VAE to force it generate new latent variables that are similar
with that of training samples. To be able to calculate anomaly score in two
feature spaces, we train a re-encoder to transform the generated data to a new
latent space. For better handling the time series, we use the LSTM as the
encoder and decoder part of the VAE framework. Experimental results of several
benchmarks show that our method outperforms state-of-the-art anomaly detection
methods.
