Forecasting crude oil price with multiscale denoising ensemble model

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

5 Scopus Citations
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Author(s)

  • Xia Li
  • Kaijian He
  • Kin Keung Lai
  • Yingchao Zou

Related Research Unit(s)

Detail(s)

Original languageEnglish
Article number716571
Journal / PublicationMathematical Problems in Engineering
Volume2014
Online published8 May 2014
Publication statusPublished - 2014

Link(s)

Abstract

Crude oil price becomes more volatile and sensitive to increasingly diversified influencing factors with higher level of deregulations worldwide. Current methodologies are being challenged as they have been constrained by traditional approaches assuming homogeneous time horizons and investment strategies. Approximations they provided over the long term time horizon no longer satisfy the accuracy requirement at shorter term and more microlevels. This paper proposes a novel crude oil price forecasting model based on the wavelet denoising ARMA models ensemble by least square support vector regression with the reduced forecasting matrix dimensions by independent component analysis. The proposed methodology combines the multi resolution analysis and nonlinear ensemble framework. The wavelet denoising based algorithm is introduced to separate and extract the underlying data components with distinct features, corresponding to investors with different investment scales, which are modeled with time series models of different specifications and parameters. Then least square support vector regression is introduced to nonlinearly ensemble results based on different wavelet families to further reduce the estimation biases and improve the forecasting generalizability. Empirical studies show the significant performance improvement when the proposed model is tested against the bench-mark models. © 2014 Xia Li et al.

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Citation Format(s)

Forecasting crude oil price with multiscale denoising ensemble model. / Li, Xia; He, Kaijian; Lai, Kin Keung; Zou, Yingchao.

In: Mathematical Problems in Engineering, Vol. 2014, 716571, 2014.

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

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