@article{M50950FCD,
title = "A GCN-based Time-Series Data Anomaly Detection Method using Sensor-specific Time Lagged Cross Correlation",
journal = "Journal of KIISE, JOK",
year = "2023",
issn = "2383-630X",
doi = "10.5626/JOK.2023.50.9.805",
author = "Kangwoo Lee,Yunyeong Kim,Sungwon Jung",
keywords = "time series data,anomaly detection,correlation analysis,TLCC,GCN",
abstract = "Anomaly detection of equipment through time series data is a very important because it can prevent further damage and contribute to productivity improvement. Although research studies on time series data anomaly detection are being actively conducted, but they have the following restrictions. First, unnecessary false alarms occur because correlations with other sensors are not considered. Second, although complete graph modeling and GAT have been applied to analyze the correlation of each sensor, this method requires a lot of time due to the increase in unnecessary operations. In this paper, we propose SC-GCNAD(Sensor-specific Correlation GCN Anomaly Detection) to address these problems. SC-GCNAD can analyze the exact correlation of each sensor by applying TLCC that reflects characteristics of time series data. It utilize GCN with excellent model expressiveness. As a result, SC-GCNAD can improve F1-Score by up to 6.37% and reduce analysis time by up to 95.31% compared to the baseline model."
}