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

Ahmad M Awajan, Mohd Tahir Ismail and S Al Wadi

PLOS ONE, 2018, vol. 13, issue 7, 1-20

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.

Date: 2018
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Persistent link: https://EconPapers.repec.org/RePEc:plo:pone00:0199582

DOI: 10.1371/journal.pone.0199582

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