Crude oil prediction using a hybrid radial basis function network

Title

Crude oil prediction using a hybrid radial basis function network

Creator

Kumar Chandar S.; Sumathi M.; Sivanandam S.N.

Description

In the recent years, the crude oil is one of the most important commodities worldwide. This paper discusses the prediction of crude oil using artificial neural networks techniques. The research data used in this study is from 1st Jan 2000- 31st April 2014. Normally, Crude oil is related with other commodities. Hence, in this study, the commodities like historical datas of gold prices, Standard & Poors 500 stock index (S & P 500) index and foreign exchange rate are considered as inputs for the network. A radial basis function is better than the back propagation network in terms of classification and learning speed. When creating a radial basis functions, the factors like number of radial basis neurons, radial layers spread constant are taken into an account. The spread constant is determined using a bio inspired particle swarm optimization algorithm. A hybrid Radial Basis Function is proposed for forecasting the crude oil prices. The accuracy measures like Mean Square Error, Mean Absolute Error, Sum Square Error and Root Mean Square Error are used to access the performance. From the results, it is clear that hybrid radial basis function outperforms the other models. 2005 - 2015 JATIT & LLS. All rights reserved.

Source

Journal of Theoretical and Applied Information Technology, Vol-72, No. 2, pp. 199-207.

Date

2015-01-01

Publisher

Asian Research Publishing Network

Subject

Crude oil prices; Hybrid radial basis function; Particle swarm optimization; Standard & Poors 500 stock index

Coverage

Kumar Chandar S., Madurai Kamaraj University, Madurai/Christ University, Bangalore, India; Sumathi M., Sri Meenakshi Government College For Arts For Women (Autonomous), Madurai, India; Sivanandam S.N., Karpagam College Of Engineering, Coimbatore, India

Rights

Restricted Access

Relation

ISSN: 19928645

Format

Online

Language

English

Type

Article

Identifier

https://www.scopus.com/inward/record.uri?eid=2-s2.0-84923123175&partnerID=40&md5=0348d0097b87f3b4f24ab4a27e4ca105