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

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.

SUBMITTER: Awajan AM

PROVIDER: S-EPMC6049912 | biostudies-literature | 2018

REPOSITORIES: biostudies-literature

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

Awajan Ahmad M AM Ismail Mohd Tahir MT Al Wadi S S

PloS one 20180717 7

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 ...[more]

PMID: 30016323

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