A HYBRID GMDH AND LEAST SQUARES SUPPORT VECTOR MACHINES IN TIME SERIES FORECASTING

被引：10

作者：

Samsudin, R. ^{[1
]}

Saad, P. ^{[1
]}

Shabri, A. ^{[2
]}

机构：

[1] Fac Comp Sci & Informat Syst, Skudai 81310, Johor, Malaysia

[2] Univ Teknol Malaysia, Fac Sci, Skudai 81310, Johor, Malaysia

来源：

NEURAL NETWORK WORLD | 2011年 / 21卷 / 03期

关键词：

Group method of data handling; least square support vector machine; autoregressive integrated moving average; neural network; Box-Jenkins method; PARTICLE SWARM OPTIMIZATION; NEURAL-NETWORK; MODEL; ARIMA; PREDICTION; ALGORITHM; DESIGN;

D O I：

10.14311/NNW.2011.21.015

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

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.

引用

页码：251 / 268

页数：18

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