Anomaly Detection on Graph Time Series

Daniel Hsu

Anomaly Detection on Graph Time Series

Machine Learning 2022-05-31 v3 Neural and Evolutionary Computing Machine Learning

Authors: Daniel Hsu

Abstract

In this paper, we use variational recurrent neural network to investigate the anomaly detection problem on graph time series. The temporal correlation is modeled by the combination of recurrent neural network (RNN) and variational inference (VI), while the spatial information is captured by the graph convolutional network. In order to incorporate external factors, we use feature extractor to augment the transition of latent variables, which can learn the influence of external factors. With the target function as accumulative ELBO, it is easy to extend this model to on-line method. The experimental study on traffic flow data shows the detection capability of the proposed method.

Keywords

temporal graph anomaly detection graph neural network

Cite

@article{arxiv.1708.02975,
  title  = {Anomaly Detection on Graph Time Series},
  author = {Daniel Hsu},
  journal= {arXiv preprint arXiv:1708.02975},
  year   = {2022}
}

Comments

Very preminary work with some fatal mistakes. Some other work covering this will appear soon

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