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

시계열데이터는 ARMA 분석에 적합지 않은 요소를 내재하고 있는 경우가 있다. 특히 선형성과 주기성을 가진 요소가 확률적인 분포와 자주 혼재 되어 있다. 이 논문에서는 이런 선형적 주기적 요소를 찾아내고 분석하는 방법을 제시한다. 특히 주기적 요소는 여러 주기가 층층이 겹쳐져서 나타난다， 주기 간에 는 서로 일정 정수비율을 유지하며 ， 한 주기 안에 다른 주기가 내 포되어 있는 경우(nested periods)가 많다. 시간규모(time-scale) 개

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