This Github repository hosts our code and pre-processed data to train a VAE-LSTM hybrid model for anomaly detection, as proposed in our paper: Anomaly Detection for Time Series Using VAE-LSTM Hybrid Model. Shuyu Lin1,Ronald Clark2, Robert Birke3, Sandro Schönborn3, Niki Trigoni1,Stephen...
In short, our anomaly detection model contains: a VAE unit which summarizes the local information of a short window into a low-dimensional embedding, a LSTM model, which acts on the low- dimensional embeddings produced by the VAE model, to manage the sequential patterns over longer term. An ...
In this work, we propose a VAE-LSTM hybrid model as an unsupervised approach for anomaly detection in time series. Our model utilizes both a VAE module for forming robust local features over short windows and a LSTM module for estimating the long term correlation in the series on top of the...
Time series anomaly detection is widely used to monitor the equipment sates through the data collected in the form of time series. At present, the deep learning method based on generative adversarial networks (GAN) has emerged for time series anomaly detection. However, this method needs to find...
MLP_VAE, Anomaly Detection, LSTM_VAE, Multivariate Time-Series Anomaly Detection, IndRNN_VAE, Tensorflow - SchindlerLiang/VAE-for-Anomaly-Detection