Joint Exponential Smoothing and Trend-based Principal Component Analysis for Anomaly Detection in Wireless Sensor Networks

무선 센서 네트워크에서의 이상 징후 감지를 위한 공동 지수 평활법 및 추세 기반 주성분 분석

Principal Component Analysis (PCA) is a powerful technique in data analysis and widely used to detect anomalies in Wireless Sensor Networks. However, the performance of conventional PCA is not high on time-series data collected by sensors. In this paper, we propose a Joint Exponential Smoothing and Trend-based Principal Component Analysis (JES-TBPCA) for Anomaly Detection which is based on conventional PCA. Experimental results on a real dataset show a remarkably higher performance of JES-TBPCA comparing to conventional PCA model in detection of stuck-at and offset anomalies.