Daily crude oil price forecasting using hybridizing wavelet and artificial neural network model
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). Th...
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my.utm.522832018-09-17T04:01:36Z http://eprints.utm.my/id/eprint/52283/ Daily crude oil price forecasting using hybridizing wavelet and artificial neural network model Shabri, Ani Samsudin, Ruhaidah Q Science 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 Hindawi Publishing Corporation 2014 Article PeerReviewed application/pdf en http://eprints.utm.my/id/eprint/52283/1/AniShabri2014_DailyCrudeOilForecastingUsingHybriding.pdf Shabri, Ani and Samsudin, Ruhaidah (2014) Daily crude oil price forecasting using hybridizing wavelet and artificial neural network model. Mathematical Problems in Engineering . ISSN 1024-123X http://dx.doi.org/10.1155/2014/201402 DOI: 10.1155/2014/201402 |
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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 |
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Article |
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Shabri, Ani Samsudin, Ruhaidah |
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Shabri, Ani Samsudin, Ruhaidah |
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Shabri, Ani |
title |
Daily crude oil price forecasting using hybridizing wavelet and artificial neural network model |
title_short |
Daily crude oil price forecasting using hybridizing wavelet and artificial neural network model |
title_full |
Daily crude oil price forecasting using hybridizing wavelet and artificial neural network model |
title_fullStr |
Daily crude oil price forecasting using hybridizing wavelet and artificial neural network model |
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Daily crude oil price forecasting using hybridizing wavelet and artificial neural network model |
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daily crude oil price forecasting using hybridizing wavelet and artificial neural network model |
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Hindawi Publishing Corporation |
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2014 |
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http://eprints.utm.my/id/eprint/52283/1/AniShabri2014_DailyCrudeOilForecastingUsingHybriding.pdf http://eprints.utm.my/id/eprint/52283/ http://dx.doi.org/10.1155/2014/201402 |
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