A hybrid GMDH and least squares support vector machines in time series forecasting
Time series consists of complex nonlinear and chaotic patterns that are difficult to forecast. This paper proposes a novel hybrid forecasting model which combines the group method of data handling (GMDH) and the least squares support vector machine (LSSVM), known as GLSSVM. The GMDH is used to deter...
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2011
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my.utm.285952019-10-31T10:10:06Z http://eprints.utm.my/id/eprint/28595/ A hybrid GMDH and least squares support vector machines in time series forecasting Samsudin, Ruhaidah Saad, Puteh Shabri, Ani QA75 Electronic computers. Computer science Time series consists of complex nonlinear and chaotic patterns that are difficult to forecast. This paper proposes a novel hybrid forecasting model which combines the group method of data handling (GMDH) and the least squares support vector machine (LSSVM), known as GLSSVM. The GMDH is used to determine the useful input variables for the LSSVM model and the LSSVM model that works as time series forecasting. Three well-known time series data sets are used in this study to demonstrate the effectiveness of the forecasting model. These data are utilized to forecast through an application aimed to handle real life time series. The results found by the proposed model were compared with the results of the GMDH and LSSVM models. Experiment result indicates that the hybrid model was a powerful tool to model time series data and provides a promising technique in time series forecasting methods. Institute of Computer Science 2011-01 Article PeerReviewed Samsudin, Ruhaidah and Saad, Puteh and Shabri, Ani (2011) A hybrid GMDH and least squares support vector machines in time series forecasting. Neural Network World, 21 (3). pp. 251-268. ISSN 1210-0552 http://dx.doi.org/10.14311/NNW.2011.21.015 DOI:10.14311/NNW.2011.21.015 |
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QA75 Electronic computers. Computer science Samsudin, Ruhaidah Saad, Puteh Shabri, Ani A hybrid GMDH and least squares support vector machines in time series forecasting |
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Time series consists of complex nonlinear and chaotic patterns that are difficult to forecast. This paper proposes a novel hybrid forecasting model which combines the group method of data handling (GMDH) and the least squares support vector machine (LSSVM), known as GLSSVM. The GMDH is used to determine the useful input variables for the LSSVM model and the LSSVM model that works as time series forecasting. Three well-known time series data sets are used in this study to demonstrate the effectiveness of the forecasting model. These data are utilized to forecast through an application aimed to handle real life time series. The results found by the proposed model were compared with the results of the GMDH and LSSVM models. Experiment result indicates that the hybrid model was a powerful tool to model time series data and provides a promising technique in time series forecasting methods. |
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Article |
author |
Samsudin, Ruhaidah Saad, Puteh Shabri, Ani |
author_facet |
Samsudin, Ruhaidah Saad, Puteh Shabri, Ani |
author_sort |
Samsudin, Ruhaidah |
title |
A hybrid GMDH and least squares support vector machines in time series forecasting |
title_short |
A hybrid GMDH and least squares support vector machines in time series forecasting |
title_full |
A hybrid GMDH and least squares support vector machines in time series forecasting |
title_fullStr |
A hybrid GMDH and least squares support vector machines in time series forecasting |
title_full_unstemmed |
A hybrid GMDH and least squares support vector machines in time series forecasting |
title_sort |
hybrid gmdh and least squares support vector machines in time series forecasting |
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Institute of Computer Science |
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2011 |
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http://eprints.utm.my/id/eprint/28595/ http://dx.doi.org/10.14311/NNW.2011.21.015 |
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