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A Hilbert-Huang Transform Approach Combined with PCA for Predicting a Time Series
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 Title & Authors
A Hilbert-Huang Transform Approach Combined with PCA for Predicting a Time Series
Park, Min-Jeong;
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A time series can be decomposed into simple components with a multiscale method. Empirical mode decomposition(EMD) is a recently invented multiscale method in Huang et al. (1998). It is natural to apply a classical prediction method such a vector autoregressive(AR) model to the obtained simple components instead of the original time series; in addition, a prediction procedure combining a classical prediction model to EMD and Hilbert spectrum is proposed in Kim et al. (2008). In this paper, we suggest to adopt principal component analysis(PCA) to the prediction procedure that enables the efficient selection of input variables among obtained components by EMD. We discuss the utility of adopting PCA in the prediction procedure based on EMD and Hilbert spectrum and analyze the daily worm account data by the proposed PCA adopted prediction method.
Multiscale method;empirical mode decomposition;Hilbert spectrum;Hilbert-Huang transform;principal component analysis;vector AR model;
 Cited by
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Huang, N. E., Shen, Z., Long, S. R., Wu, M. C., Shih, H. H., Zheng, Q., Yen, N. C., Tung, C. C. and Liu, H. H. (1998). The empirical mode decomposition and Hilbert spectrum for nonlinear and nonstationary time series analysis, Proceeding of the Royal Society London A, 454, 903-995. crossref(new window)

Kim, D., Paek, S.-H. and Oh, H.-S. (2008). A Hilbert-Huang transform approach for predicting cyber-attacks, Journal of the Korean Statistical Society, 37, 277-283. crossref(new window)

Oh, H.-S., Suh, J. H. and Kim, D. (2009). A multi-resolution approach to non-stationary financial time series using the Hilbert-Huang transform, The Korean Journal of Applied Statistics, 22, 499-513. crossref(new window)