Forecasting Malaysia load using a hybrid model
A hybrid model, which combines the seasonal time series ARIMA (SARIMA) and the multilayer feed-forward neural network to forecast time series with seasonality, is shown to outperform both two single models. Besides the selection of transfer functions, the determination of hidden nodes to use for the...
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Uni Islam Bandung Indonesia
2010
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Online Access: | http://eprints.utm.my/id/eprint/25946/2/article.php_article%3D261535%26val%3D1587%26title%3DForecastingMalaysiaLoadUsingaHybridModel http://eprints.utm.my/id/eprint/25946/ http://download.portalgaruda.org/article.php?article=261535&val=1587&title=ForecastingMalaysiaLoadUsingaHybridModel |
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my.utm.259462017-10-13T12:27:46Z http://eprints.utm.my/id/eprint/25946/ Forecasting Malaysia load using a hybrid model Mohamed, Norizan Ahmad, Maizah Hura QA Mathematics A hybrid model, which combines the seasonal time series ARIMA (SARIMA) and the multilayer feed-forward neural network to forecast time series with seasonality, is shown to outperform both two single models. Besides the selection of transfer functions, the determination of hidden nodes to use for the non linear model is believed to improve the accuracy of the hybrid model. In this paper, we focus on the selection of the appropriate number of hidden nodes on the non linear model to forecast Malaysia load. Results show that by using only one hidden node, the hybrid model of Malaysia load performs better than both single models with mean absolute percentage error (MAPE) of less than 1%. Uni Islam Bandung Indonesia 2010-05 Article PeerReviewed text/html en http://eprints.utm.my/id/eprint/25946/2/article.php_article%3D261535%26val%3D1587%26title%3DForecastingMalaysiaLoadUsingaHybridModel Mohamed, Norizan and Ahmad, Maizah Hura (2010) Forecasting Malaysia load using a hybrid model. Jurnal STATISTIKA, 10 (1). pp. 1-8. ISSN 1411-5891 http://download.portalgaruda.org/article.php?article=261535&val=1587&title=ForecastingMalaysiaLoadUsingaHybridModel |
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QA Mathematics Mohamed, Norizan Ahmad, Maizah Hura Forecasting Malaysia load using a hybrid model |
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A hybrid model, which combines the seasonal time series ARIMA (SARIMA) and the multilayer feed-forward neural network to forecast time series with seasonality, is shown to outperform both two single models. Besides the selection of transfer functions, the determination of hidden nodes to use for the non linear model is believed to improve the accuracy of the hybrid model. In this paper, we focus on the selection of the appropriate number of hidden nodes on the non linear model to forecast Malaysia load. Results show that by using only one hidden node, the hybrid model of Malaysia load performs better than both single models with mean absolute percentage error (MAPE) of less than 1%. |
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Article |
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Mohamed, Norizan Ahmad, Maizah Hura |
author_facet |
Mohamed, Norizan Ahmad, Maizah Hura |
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Mohamed, Norizan |
title |
Forecasting Malaysia load using a hybrid model |
title_short |
Forecasting Malaysia load using a hybrid model |
title_full |
Forecasting Malaysia load using a hybrid model |
title_fullStr |
Forecasting Malaysia load using a hybrid model |
title_full_unstemmed |
Forecasting Malaysia load using a hybrid model |
title_sort |
forecasting malaysia load using a hybrid model |
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Uni Islam Bandung Indonesia |
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2010 |
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http://eprints.utm.my/id/eprint/25946/2/article.php_article%3D261535%26val%3D1587%26title%3DForecastingMalaysiaLoadUsingaHybridModel http://eprints.utm.my/id/eprint/25946/ http://download.portalgaruda.org/article.php?article=261535&val=1587&title=ForecastingMalaysiaLoadUsingaHybridModel |
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13.2014675 |