Forecasting crude oil price with an EMD-based neural network ensemble learning paradigm

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalpeer-review

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Author(s)

  • Lean Yu
  • Shouyang Wang
  • Kin Keung Lai

Related Research Unit(s)

Detail(s)

Original languageEnglish
Pages (from-to)2623-2635
Journal / PublicationEnergy Economics
Volume30
Issue number5
Publication statusPublished - Sep 2008

Abstract

In this study, an empirical mode decomposition (EMD) based neural network ensemble learning paradigm is proposed for world crude oil spot price 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 a three-layer feed-forward neural network (FNN) model was used to model each of the extracted IMFs, so that the tendencies of these IMFs could be accurately predicted. Finally, the prediction results of all IMFs are combined with an adaptive linear neural network (ALNN), to formulate an ensemble output for the original crude oil price series. For verification and testing, 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 the proposed EMD-based neural network ensemble learning methodology. Empirical results obtained demonstrate attractiveness of the proposed EMD-based neural network ensemble learning paradigm. © 2008 Elsevier B.V. All rights reserved.

Research Area(s)

  • Adaptive linear neural network, C45, C53, Crude oil price prediction, Empirical mode decomposition, Ensemble learning, Feed-forward neural network, Q49

Citation Format(s)

Forecasting crude oil price with an EMD-based neural network ensemble learning paradigm. / Yu, Lean; Wang, Shouyang; Lai, Kin Keung.

In: Energy Economics, Vol. 30, No. 5, 09.2008, p. 2623-2635.

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalpeer-review