Daily Crude Oil Price Forecasting Using Hybridizing Wavelet and Artificial Neural Network Model

被引：25

作者：

Shabri, Ani ^{[1
]}

Samsudin, Ruhaidah ^{[2
]}

机构：

[1] Univ Teknol Malaysia, Fac Sci, Dept Sci Math, Skudai 81310, Johor, Malaysia

[2] Univ Teknol Malaysia, Fac Comp, Dept Software Engn, Skudai 81310, Johor, Malaysia

来源：

MATHEMATICAL PROBLEMS IN ENGINEERING | 2014年 / 2014卷

关键词：

DECOMPOSITION; PREDICTION;

D O I：

10.1155/2014/201402

中图分类号：

T [工业技术];

学科分类号：

08 ;

摘要：

A new method based on integrating discrete wavelet transform and artificial neural networks (WANN) model for daily crude oil price forecasting is proposed. The discrete Mallat wavelet transform is used to decompose the crude price series into one approximation series and some details series (DS). The new series obtained by adding the effective one approximation series and DS component is then used as input into the ANN model to forecast crude oil price. The relative performance of WANN model was compared to regular ANN model for crude oil forecasting at lead times of 1 day for two main crude oil price series, West Texas Intermediate (WTI) and Brent crude oil spot prices. In both cases, WANN model was found to provide more accurate crude oil prices forecasts than individual ANN model.

引用

页数：10

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