Abstract
The study investigates the accuracy of bagging ensemble models (i.e., bagged artificial neural networks (BANN) and bagged regression trees (BRT)) in monthly crude oil price forecasting. Two ensemble models are obtained by coupling bagging and two simple machine learning models (i.e., artificial neural networks (ANN) and classification and regression trees (CART)) and results are compared with those of the single ANN and CART models. Analytical results suggest that ANN based models (ANN & BANN) are superior to tree-based models (RT & BRT) and the bagging ensemble method could optimize the forecast accuracy of the both single ANN and CART models in monthly crude oil price forecasting. Key words: Artificial neural networks, bagging (bootstrap aggregating), classification and regression trees, ensemble models, forecasting.
Highlights
Oil is an important component of the economic activity and the adverse effect of the crude oil prices on the level of the output is widely recognized in numerous empirical studies (Hamilton, 1983; Hamilton and Herrera, 2004; Huntington, 2005; Barsky and Kilian, 2004; Kilian, 2008)
We offer a better forecasting method for oil price, so we run the program for the each parameter values specified above and select giving the best value
Ensemble learning is the supervised learning from the information generated by the base predictors
Summary
Oil is an important component of the economic activity and the adverse effect of the crude oil prices on the level of the output is widely recognized in numerous empirical studies (Hamilton, 1983; Hamilton and Herrera, 2004; Huntington, 2005; Barsky and Kilian, 2004; Kilian, 2008). As the traditional and econometric models have some limitations, some non-linear and emerging artificial intelligent models like artificial neural networks (ANN), support vector machines (SVM) and genetic programming (GP) can provide powerful solutions to nonlinear crude oil prediction. Abramson and Finizza (1991) attempted to predict crude oil prices using neural network models. Yu et al (2008) proposed using an empirical mode decomposition (EMD) based neural network ensemble learning paradigm for crude oil forecasting.
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