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Anomaly detection is a classical but worthwhile problem, and many deeplearning-based anomaly detection algorithms have been proposed, which canusually achieve better detection results than traditional methods. In view ofreconstruct ability of the model and the calculation of anomaly score, thispaper proposes a time series anomaly detection method based on VariationalAutoEncoder model(VAE) with re-Encoder and Latent Constraint network(VELC). Inorder to modify reconstruct ability of the model to prevent it fromreconstructing abnormal samples well, we add a constraint network in the latentspace of the VAE to force it generate new latent variables that are similarwith that of training samples. To be able to calculate anomaly score in twofeature spaces, we train a re-encoder to transform the generated data to a newlatent space. For better handling the time series, we use the LSTM as theencoder and decoder part of the VAE framework. Experimental results of severalbenchmarks show that our method outperforms state-of-the-art anomaly detectionmethods.

VELC: A New Variational AutoEncoder Based Model for Time Series Anomaly Detection