Improving forecasting accuracy for stock market data using EMD-HW bagging.
Ontology highlight
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 |
REPOSITORIES: biostudies-literature
ACCESS DATA