a Record control number 000301947300060

a ISSN 0302-9743

a Original cataloging agency NEU GrandLibrary

a Language code of text/sound track or separate title eng

a Classification number QA76

b Item number .S87 2011

a Personal name Khashman, Adnan,

a Title Support Vector Machines versus Back Propagation Algorithm for Oil Price Prediction.

h Medium Electronic Document

c Statement of responsibility, etc Adnan Khashman, Nnamdi I. Nwulu.

c Date of publication, distribution, etc 2011.

b Name of publisher, distributor, etc Springer,

a Place of publication, distribution, etc Berlin :

a Summary, etc The importance of crude oil in the world economy has made it imperative that efficient models be designed for predicting future prices. Neural networks can be used as prediction models, thus, in this paper we investigate and compare the use of a support vector machine and a back propagation neural network for the task of predicting oil prices. We also present a novel method of representing the oil price data as input data to the neural networks by defining input economic and seasonal indicators which could affect the oil price. The oil price database is publicly available online and can be obtained from the West Texas Intermediate crude oil price dataset spanning a period of 24 years. Experimental results suggest the neural networks can be efficiently used to predict future oil prices with minimal computational expense.

a Topical term or geographic name as entry element Near East University Article

a Topical term or geographic name as entry element Yakın Doğu Üniversitesi Makale

a Topical term or geographic name as entry element Neural networks (Computer science)

a Topical term or geographic name as entry element Price Prediction

a Topical term or geographic name as entry element Crude Oil

g Relationship information 2011, Vol 6677, p530-538

t Title Advances In Neural Networks - ISNN 2011, PT III

x International Standard Serial Number 03029743

u Uniform Resource Identifier http://library.neu.edu.tr:2048/login?url=http://dx.doi.org/10.1007/978-3-642-21111-9_60