.png){kind=icon}
返回
Correlation-Aware Spatial-Temporal Graph Learning for Multivariate Time-Series Anomaly Detection
.png){kind=icon}
OA
AI
DOI：10.1109/TNNLS.2023.3325667.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Multivariate time-series anomaly detection is critically important in many applications, including retail, transportation, power grid, and water treatment plants. Existing approaches for this problem mostly employ either statistical models which cannot capture the nonlinear relations well or conventional deep learning (DL) models e.g., convolutional neural network (CNN) and long short-term memory (LSTM) that do not explicitly learn the pairwise correlations among variables. To overcome these limitations, we propose a novel method, correlation-aware spatial-temporal graph learning (termed ), for time-series anomaly detection. explicitly captures the pairwise correlations via a correlation learning (MTCL) module based on which a spatial-temporal graph neural network (STGNN) can be developed. Then, by employing a graph convolution network (GCN) that exploits one-and multihop neighbor information, our STGNN component can encode rich spatial information from complex pairwise dependencies between variables. With a temporal module that consists of dilated convolutional functions, the STGNN can further capture long-range dependence over time. A novel anomaly scoring component is further integrated into to estimate the degree of an anomaly in a purely unsupervised manner. Experimental results demonstrate that can detect and diagnose anomalies effectively in general settings as well as enable early detection across different time delays. Our code is available at https://github.com/huankoh/CST-GL.
Keyword:
Time series analysis
Anomaly detection
Data models
Graph neural networks
Pairwise error probability
Correlation
Analytical models
graph neural networks (GNNs)
multivariate time series
期刊
IF:
8.9
论文数:
7.6K
被引数:
7.2W
机构
引用论文
Kinetic Characterization of Hydrolysis of Nitrocefin, Cefoxitin, and Meropenem by β-Lactamase fromMycobacterium tuberculosis.png)
BiochemistryOAAI
BiochemistryIF0
OAAI分享收藏分享收藏Cardiomyocyte specific expression of the nuclear matrix protein, CIZ1, stimulates production of mononucleated cells with an extended window of proliferation in the postnatal mouse heartBiology Open
OAAI
Biology OpenIF0
OAAI分享收藏Self-Supervised Spatiotemporal Clustering of Vehicle Emissions With Graph Convolutional Network
PREAI
分享收藏
查看更多内容