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Improving forecasting accuracy for stock market data using EMD-HW bagging

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  • Ahmad M Awajan
  • Mohd Tahir Ismail
  • S AL Wadi

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.

Suggested Citation

  • Ahmad M Awajan & Mohd Tahir Ismail & S AL Wadi, 2018. "Improving forecasting accuracy for stock market data using EMD-HW bagging," PLOS ONE, Public Library of Science, vol. 13(7), pages 1-20, July.
    • Handle: RePEc:plo:pone00:0199582
    DOI: 10.1371/journal.pone.0199582
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