Graph Neural Network-Based Anomaly Detection in Multivariate Time Series

Authors

  • Ailin Deng National University of Singapore
  • Bryan Hooi National University of Singapore

Keywords:

Anomaly/Outlier Detection

Abstract

Given high-dimensional time series data (e.g., sensor data), how can we detect anomalous events, such as system faults and attacks? More challengingly, how can we do this in a way that captures complex inter-sensor relationships, and detects and explains anomalies which deviate from these relationships? Recently, deep learning approaches have enabled improvements in anomaly detection in high-dimensional datasets; however, existing methods do not explicitly learn the structure of existing relationships between variables, or use them to predict the expected behavior of time series. Our approach combines a structure learning approach with graph neural networks, additionally using attention weights to provide explainability for the detected anomalies. Experiments on two real-world sensor datasets with ground truth anomalies show that our method detects anomalies more accurately than baseline approaches, accurately captures correlations between sensors, and allows users to deduce the root cause of a detected anomaly.

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Published

2021-05-18

How to Cite

Deng, A., & Hooi, B. (2021). Graph Neural Network-Based Anomaly Detection in Multivariate Time Series. Proceedings of the AAAI Conference on Artificial Intelligence, 35(5), 4027-4035. Retrieved from https://ojs.aaai.org/index.php/AAAI/article/view/16523

Issue

Section

AAAI Technical Track on Data Mining and Knowledge Management