Control Limits of Time Series Data using Hilbert-Huang Transform : Dealing with Nested Periods
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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.
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.
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