Unknown

Dataset Information

0

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 |

REPOSITORIES: biostudies-literature

Similar Datasets

| S-EPMC7517323 | biostudies-literature
| S-EPMC3926375 | biostudies-other
| S-EPMC6377125 | biostudies-literature
| S-EPMC6751183 | biostudies-literature
| S-EPMC9455286 | biostudies-literature
| S-EPMC4388524 | biostudies-literature
| S-EPMC7738547 | biostudies-literature
| S-EPMC10936758 | biostudies-literature
| S-EPMC9970056 | biostudies-literature
| S-EPMC5219605 | biostudies-literature