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

TL;DR

This paper introduces VELC, a novel variational autoencoder-based model with a re-encoder and latent constraint network for improved time series anomaly detection, demonstrating superior performance over existing methods.

Contribution

The paper proposes a new VAE-based approach with a latent constraint and re-encoder to enhance anomaly detection in time series data, addressing reconstruction issues of abnormal samples.

Findings

Outperforms state-of-the-art anomaly detection methods on benchmark datasets.

Uses LSTM-based encoder and decoder for better time series handling.

Incorporates a latent constraint to prevent normal samples from being reconstructed as anomalies.

Abstract

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.