Hybridizing GMDH and least squares SVM support vector machine for forecasting tourism demand

In this paper, we proposed a novel hybrid group method of data handling least squares support vector machine (GLSSVM) algorithm, which combines the theory a group method of data handling (GMDH) with the least squares support vector machine (LSSVM). With the GMDH is used to determine the inputs of LS...

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Main Authors: Samsudin, Ruhaidah, Saad, Puteh, Shabri, Ani
Format: Article
Language:English
Published: Academic Research Publishing Agency 2010
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Online Access:http://eprints.utm.my/id/eprint/37838/2/IJRRAS_3_3_06.pdf
http://eprints.utm.my/id/eprint/37838/
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Institution: Universiti Teknologi Malaysia
Language: English
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spelling my.utm.378382017-02-15T01:18:34Z http://eprints.utm.my/id/eprint/37838/ Hybridizing GMDH and least squares SVM support vector machine for forecasting tourism demand Samsudin, Ruhaidah Saad, Puteh Shabri, Ani QA75 Electronic computers. Computer science In this paper, we proposed a novel hybrid group method of data handling least squares support vector machine (GLSSVM) algorithm, which combines the theory a group method of data handling (GMDH) with the least squares support vector machine (LSSVM). With the GMDH is used to determine the inputs of LSSVM method and the LSSVM model which works as time series forecasting. The aim of this study is to examine the feasibility of the hybrid model in tourism demand forecasting by comparing it with GMDH and LSSVM model. The tourist arrivals to Johor Malaysia during 1970 to 2008 were employed as the data set. The comparison of modeling results demonstrate that the hybrid model outperforms than two other nonlinear approaches GMDH and LSSVM models. Academic Research Publishing Agency 2010-06 Article PeerReviewed text/html en http://eprints.utm.my/id/eprint/37838/2/IJRRAS_3_3_06.pdf Samsudin, Ruhaidah and Saad, Puteh and Shabri, Ani (2010) Hybridizing GMDH and least squares SVM support vector machine for forecasting tourism demand. International Journal of Research and Reviews in Applied Sciences (IJRRAS), 3 (3). pp. 274-279. ISSN 2076-734X (Print); 2076-7366 (Online)
institution Universiti Teknologi Malaysia
building UTM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Teknologi Malaysia
content_source UTM Institutional Repository
url_provider http://eprints.utm.my/
language English
topic QA75 Electronic computers. Computer science
spellingShingle QA75 Electronic computers. Computer science
Samsudin, Ruhaidah
Saad, Puteh
Shabri, Ani
Hybridizing GMDH and least squares SVM support vector machine for forecasting tourism demand
description In this paper, we proposed a novel hybrid group method of data handling least squares support vector machine (GLSSVM) algorithm, which combines the theory a group method of data handling (GMDH) with the least squares support vector machine (LSSVM). With the GMDH is used to determine the inputs of LSSVM method and the LSSVM model which works as time series forecasting. The aim of this study is to examine the feasibility of the hybrid model in tourism demand forecasting by comparing it with GMDH and LSSVM model. The tourist arrivals to Johor Malaysia during 1970 to 2008 were employed as the data set. The comparison of modeling results demonstrate that the hybrid model outperforms than two other nonlinear approaches GMDH and LSSVM models.
format Article
author Samsudin, Ruhaidah
Saad, Puteh
Shabri, Ani
author_facet Samsudin, Ruhaidah
Saad, Puteh
Shabri, Ani
author_sort Samsudin, Ruhaidah
title Hybridizing GMDH and least squares SVM support vector machine for forecasting tourism demand
title_short Hybridizing GMDH and least squares SVM support vector machine for forecasting tourism demand
title_full Hybridizing GMDH and least squares SVM support vector machine for forecasting tourism demand
title_fullStr Hybridizing GMDH and least squares SVM support vector machine for forecasting tourism demand
title_full_unstemmed Hybridizing GMDH and least squares SVM support vector machine for forecasting tourism demand
title_sort hybridizing gmdh and least squares svm support vector machine for forecasting tourism demand
publisher Academic Research Publishing Agency
publishDate 2010
url http://eprints.utm.my/id/eprint/37838/2/IJRRAS_3_3_06.pdf
http://eprints.utm.my/id/eprint/37838/
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