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http://dx.doi.org/10.5351/KJAS.2006.19.2.319

Hierarchical Smoothing Technique by Empirical Mode Decomposition  

Kim Dong-Hoh (Department of International Management, Hongik University)
Oh Hee-Seok (Department of Statistics, Seoul National University)
Publication Information
The Korean Journal of Applied Statistics / v.19, no.2, 2006 , pp. 319-330 More about this Journal
Abstract
A signal in real world usually composes of multiple signals having different scales of frequencies. For example sun-spot data is fluctuated over 11 year and 85 year. Economic data is supposed to be compound of seasonal component, cyclic component and long-term trend. Decomposition of the signal is one of the main topics in time series analysis. However when the signal is subject to nonstationarity, traditional time series analysis such as spectral analysis is not suitable. Huang et. at(1998) proposed data-adaptive method called empirical mode decomposition (EMD) . Due to its robustness to nonstationarity, EMD has been applied to various fields. Huang et. at, however, have not considered denoising when data is contaminated by error. In this paper we propose efficient denoising method utilizing cross-validation.
Keywords
Decomposition; Empirical mode decomposition; Frequency; Implicit mode function; Sifting; Zero-crossing;
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