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
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Official URL: http://www.nnw.cz/obsahy11.html#3-2011
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.
|Uncontrolled Keywords:||autoregrcssive integrated moving average, box-jenkins method, group method of data handling, least square support vector machine, neural network|
|Subjects:||Q Science > QA Mathematics > QA75 Electronic computers. Computer science|
|Divisions:||Computer Science and Information System (Formerly known)|
|Deposited By:||Liza Porijo|
|Deposited On:||25 Oct 2012 07:14|
|Last Modified:||13 Feb 2017 02:35|
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