[논문]힐버트-황 변환을 이용한 시계열 데이터 관리한계 : 중첩주기의 사례

서정열; 이세재

doi:10.11627/jkise.2014.37.4.35

힐버트-황 변환을 이용한 시계열 데이터 관리한계 : 중첩주기의 사례
Control Limits of Time Series Data using Hilbert-Huang Transform : Dealing with Nested Periods 원문보기

Journal of Korean Society of Industrial and Systems Engineering = 한국산업경영시스템학회지, v.37 no.4, 2014년, pp.35 - 41

서정열 (금오공과대학교 산업공학부) , 이세재 (금오공과대학교 산업공학부)

Abstract ▼ AI-Helper

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.

주제어

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가설 설정

We will combine LSSA, FFT, and HHT to extract a periodic component underlying the time series. There are two criteria in selecting these methods : a) the process should be efficient (not time-consuming) b) the introduction of rather subjective external parameters should be kept to a minimum.

제안 방법

For example in time-series data showing multiple periods of varying lengths, we need more than the autoregressive integrated moving average (ARIMA henceforth) model to help address the additional periodicity problem. Assuming no further complications than periodicity, in this paper, we discuss how to identify, estimate and extract these periodic components with varying time-scales, and subtract them from the original time-series data. The resulting data can then be modeled with ARIMA (in our case, ARMA).

후속연구

This study might be examined with some further investigations using more cases of time series. The proper values for UCL and LCL are those which need further empirical investigations. Applying this method to real-world time-series data, detailed discussion on the results would take up too much additional space.

참고문헌 (17)

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상세보기
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