Affiliations 

  • 1 Department of Mathematics, Al Hussien bin Talal University, Ma'an, Jordan
  • 2 School of Mathematical Sciences, University Science Malaysia, Penang, Malaysia
  • 3 Department of Risk Management and Insurance, The University of Jordan, Amman, Jordan
PLoS One, 2018;13(7):e0199582.
PMID: 30016323 DOI: 10.1371/journal.pone.0199582

Abstract

Many researchers documented that the stock market data are nonstationary and nonlinear time series data. In this study, we use EMD-HW bagging method for nonstationary and nonlinear time series forecasting. The EMD-HW bagging method is based on the empirical mode decomposition (EMD), the moving block bootstrap and the Holt-Winter. The stock market time series of six countries are used to compare EMD-HW bagging method. This comparison is based on five forecasting error measurements. The comparison shows that the forecasting results of EMD-HW bagging are more accurate than the forecasting results of the fourteen selected methods.

* Title and MeSH Headings from MEDLINE®/PubMed®, a database of the U.S. National Library of Medicine.