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}
TY - GEN
T1 - STGAT-MAD: Spatial-Temporal Graph Attention Network for Multivariate Time Series Anomaly Detection
AU - Zhan, Jun
AU - Wang, Siqi
AU - Ma, Xiandong
AU - Wu, Chengkun
AU - Yang, Canqun
AU - Zeng, Detian
AU - Wang, Shilin
N1 - ©2022 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.
PY - 2022/4/27
Y1 - 2022/4/27
N2 - Anomaly detection in multivariate time series data is challenging due to complex temporal and feature correlations and heterogeneity. This paper proposes a novel unsupervised multi-scale stacked spatial-temporal graph attention network for multivariate time series anomaly detection (STGATMAD). The core of our framework is to coherently capture the feature and temporal correlations among multivariate time-series data with stackable STGAT networks. Meanwhile, a multi-scale input network is exploited to capture the temporal correlations in different time-scales. Experiments on a new wind turbine dataset (built and released by us) and three public datasets show that our method detects anomalies more accurately than baseline approaches and provide interpretability through observing the attention score among multiple sensors and different times.
AB - Anomaly detection in multivariate time series data is challenging due to complex temporal and feature correlations and heterogeneity. This paper proposes a novel unsupervised multi-scale stacked spatial-temporal graph attention network for multivariate time series anomaly detection (STGATMAD). The core of our framework is to coherently capture the feature and temporal correlations among multivariate time-series data with stackable STGAT networks. Meanwhile, a multi-scale input network is exploited to capture the temporal correlations in different time-scales. Experiments on a new wind turbine dataset (built and released by us) and three public datasets show that our method detects anomalies more accurately than baseline approaches and provide interpretability through observing the attention score among multiple sensors and different times.
KW - Multivariate Time Series
KW - Anomaly detection
KW - Spatial-Temporal Graph Attention Network
U2 - 10.1109/ICASSP43922.2022.9747274
DO - 10.1109/ICASSP43922.2022.9747274
M3 - Conference contribution/Paper
BT - (ICASSP 2022) 2022 IEEE International Conference on Acoustics, Speech and Signal Processing, May 22-27, 2022, Singapore
PB - IEEE
ER -