Empirical Mode Decomposition Combined with Local Linear Quantile Regression for Automatic Boundary CorrectionReport as inadecuate




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Abstract and Applied Analysis - Volume 2014 2014, Article ID 731827, 8 pages -

Research Article

School of Mathematical Sciences, Universiti Sains Malaysia, 11800 Minden, Penang, Malaysia

Statistics Department, Sebha University, Sebha 00218, Libya

Received 22 November 2013; Revised 30 January 2014; Accepted 4 February 2014; Published 25 March 2014

Academic Editor: Biren N. Mandal

Copyright © 2014 Abobaker M. Jaber et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

Empirical mode decomposition EMD is particularly useful in analyzing nonstationary and nonlinear time series. However, only partial data within boundaries are available because of the bounded support of the underlying time series. Consequently, the application of EMD to finite time series data results in large biases at the edges by increasing the bias and creating artificial wiggles. This study introduces a new two-stage method to automatically decrease the boundary effects present in EMD. At the first stage, local polynomial quantile regression LLQ is applied to provide an efficient description of the corrupted and noisy data. The remaining series is assumed to be hidden in the residuals. Hence, EMD is applied to the residuals at the second stage. The final estimate is the summation of the fitting estimates from LLQ and EMD. Simulation was conducted to assess the practical performance of the proposed method. Results show that the proposed method is superior to classical EMD.





Author: Abobaker M. Jaber, Mohd Tahir Ismail, and Alssaidi M. Altaher

Source: https://www.hindawi.com/



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