Forecasting crude oil demand using a hybrid SVR and Markov approach

Research output: Chapters, Conference Papers, Creative and Literary Works (RGC: 12, 32, 41, 45)12_Chapter in an edited book (Author)Not applicablepeer-review

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Original languageEnglish
Title of host publicationBusiness Intelligence in Economic Forecasting: Technologies and Techniques
PublisherIGI Global
Pages235-247
ISBN (Print)9781615206292
Publication statusPublished - 2010

Abstract

In this chapter, a hybrid support vector regression (SVR) and Markov forecasting approach is proposed for energy demand prediction. The original time series of energy demand is firstly decomposed into the general trend series and the random fluctuation series. Then the SVR method is used to model the general trend series and the Markov forecasting method is used to model the random fluctuation series so that the tendencies of two series can be accurately predicted. The prediction results of two series are integrated to formulate an ensemble output for future energy demand. The proposed forecasting approach makes full use of the historical time series information so as to improve the forecasting precision of time series with large random fluctuation. To illustrate the applicability and capability of the proposed approach, it is used to analyze and forecast world crude oil demand. For verification, the proposed approach is compared with SVR method, Markov forecasting method and ARIMA. The results show that the hybrid SVR and Markov forecasting approach proposed in this chapter can be applied successfully and provide high accuracy and reliability for forecasting world crude oil demand. © 2010, IGI Global.

Citation Format(s)

Forecasting crude oil demand using a hybrid SVR and Markov approach. / Xu, Wei; Wang, Jue; Ma, Jian.

Business Intelligence in Economic Forecasting: Technologies and Techniques. IGI Global, 2010. p. 235-247.

Research output: Chapters, Conference Papers, Creative and Literary Works (RGC: 12, 32, 41, 45)12_Chapter in an edited book (Author)Not applicablepeer-review