Abstract
In this study, an empirical mode decomposition (EMD) based neural network ensemble learning model is proposed for world crude oil spot price modeling and forecasting. For this purpose, the original crude oil spot price series were first decomposed into a finite and often small number of intrinsic mode functions (IMFs). Then the three-layer feed-forward neural network (FNN) model was used to model each extracted IMFs so that the tendencies of these IMFs can be accurately predicted. Finally, the prediction results of each IMFs are combined with an adaptive linear neural network (ALNN) to formulate a ensemble output for the original oil series. For verification, two main crude oil price series, West Texas Intermediate (WTI) crude oil spot price and Brent crude oil spot price are used to test the effectiveness of this proposed neural network ensemble methodology.
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Yu, L., Wang, S., Lai, K.K. (2008). An EMD-Based Neural Network Ensemble Learning Model for World Crude Oil Spot Price Forecasting. In: Prasad, B. (eds) Soft Computing Applications in Business. Studies in Fuzziness and Soft Computing, vol 230. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-79005-1_14
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DOI: https://doi.org/10.1007/978-3-540-79005-1_14
Publisher Name: Springer, Berlin, Heidelberg
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