An EMD-based neural network ensemble learning model for world crude oil spot price forecasting

Lean Yu, Shouyang Wang, Kin Keung Lai

    Research output: Chapters, Conference Papers, Creative and Literary WorksRGC 12 - Chapter in an edited book (Author)peer-review

    12 Citations (Scopus)

    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. © 2008 Springer-Verlag Berlin Heidelberg.
    Original languageEnglish
    Title of host publicationSoft Computing Applications in Business
    Pages261-271
    Volume230
    DOIs
    Publication statusPublished - 2008

    Publication series

    NameStudies in Fuzziness and Soft Computing
    Volume230
    ISSN (Print)1434-9922

    Research Keywords

    • Adaptive linear neural network
    • Crude oil price prediction
    • Empirical mode decomposition
    • Ensemble learning
    • Feed-forward neural network

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