힐버트-황 변환을 이용한 시계열 데이터 관리한계 : 중첩주기의 사례

Abstract

Real-life time series characteristic data has significant amount of non-stationary components, especially periodic componentsin nature. Extracting such components has required many ad-hoc techniques with external parameters set by users in a case-by-casemanner. In this study, we used Empirical Mode Decomposition Method from Hilbert-Huang Transform to extract them in asystematic manner with least number of ad-hoc parameters set by users. After the periodic components are removed, the remainingtime-series data can be analyzed with traditional methods such as ARIMA model. Then we suggest a different way of settingcontrol chart limits for characteristic data with periodic components in addition to ARIMA components.