A hybrid GMDH and least squares support vector machines in time series forecasting

Title:

A hybrid GMDH and least squares support vector machines in time series forecasting

Creator:

Samsudin , R., Saad, P., and Shabri , A.

Identifier:

https://cdk.lib.cas.cz/client/handle/uuid:ff721078-06e0-4c7c-964d-311947d6041c
uuid:ff721078-06e0-4c7c-964d-311947d6041c
doi:10.14311/NNW.2011.21.015

Subject:

Group method of data handling, least square support vector machine, Autoagressive integrated moving average, neural network, and Box-Jenkins method

Type:

model:article and TEXT

Format:

bez média and svazek

Description:

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.

Language:

English

Rights:

http://creativecommons.org/publicdomain/mark/1.0/
policy:public

Source:

Neural network world: international journal on neural and mass-parallel computing and information systems | 2011 Volume:21 | Number:3

Harvested from:

CDK

Metadata only:

false