# Anomaly Detection on Graph Time Series

arXiv: https://arxiv.org/abs/1708.02975 · HTML: https://mlsanity.com/paper/1708.02975/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 Aug 2017.

## Built on by (most cited first, 2 of 2)

- [Spatio-Temporal Anomaly Detection with Graph Networks for Data Quality Monitoring of the Hadron Calorimeter](https://mlsanity.com/paper/2311.04190/) (2023)
- [Data Quality Monitoring for the Hadron Calorimeters Using Transfer Learning for Anomaly Detection](https://mlsanity.com/paper/2408.16612/) (2024)

## Abstract (arXiv)

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.