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.

1. Introduction
 2. Background and Previous Research
 3. Determining Optimal Periodic Component
 4. Two-Stage Estimation Process forAnalysis
 5. Determining Control Limits
 6. Results and Discussion
 7. Conclusion
 Acknowledgement
 References

• Jung-Yul Suh(School of Industrial Engineering, Kumoh National Insitute of Technology) | 서정열
• Sae Jae Lee(School of Industrial Engineering, Kumoh National Insitute of Technology) | 이세재 Corresponding Author