Forecasting Model for Crude Oil Prices Based on Artificial Neural Networks

被引：31

作者：

Haidar, Imad ^{[1
]}

Kulkarni, Siddhivinayak ^{[1
]}

Pan, Heping ^{[2
]}

机构：

[1] Univ Ballarat, Sch Informat Technol & Math Sci, POB 663, Ballarat, Vic 3353, Australia

[2] Univ Elect Sci & Technol China, Chengdu 610054, Peoples R China

来源：

ISSNIP 2008: PROCEEDINGS OF THE 2008 INTERNATIONAL CONFERENCE ON INTELLIGENT SENSORS, SENSOR NETWORKS, AND INFORMATION PROCESSING | 2008年

关键词：

D O I：

10.1109/ISSNIP.2008.4761970

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

This paper presents short-term forecasting model for crude oil prices based on three layer feedforward neural network. Careful attention was paid on finding the optimal network structure. Moreover, a number of features were tested as an inputs such as crude oil futures prices, dollar index, gold spot price, heating oil spot price and S&P 500 index. The results show that with adequate network design and appropriate selection of the training inputs, feedforward networks are capable of forecasting noisy time series with high accuracy.

引用

页码：103 / +

页数：2

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