Forecasting tourism demand using a multifactor support vector machine model

Authors:
Ping-Feng Pai;Wei-Chiang Hong;Chih-Sheng Lin
Affiliations:
Department of Information Management, National Chi Nan University, Puli, Nantou, Taiwan, China;School of Management, Da-Yeh University, Da-Tusen, Changhua, Taiwan, China;Department of Industrial Engineering and Enterprise Information, Tung-Hai University, Taichung, Taiwan, China
Venue:
CIS'05 Proceedings of the 2005 international conference on Computational Intelligence and Security - Volume Part I
Year:
2005

Citing 5
Cited 0

The nature of statistical learning theory

The nature of statistical learning theory
Artificial Neural Networks

Artificial Neural Networks
A cointegration analysis of annual tourism demand by Malaysia for Australia

Mathematics and Computers in Simulation - Selected papers of the MSSANZ/IMACS 13th biennial conference on modelling and simulation, Hamilton, New Zealand, December 1999
Support Vector Mixture for Classification and Regression Problems

ICPR '98 Proceedings of the 14th International Conference on Pattern Recognition-Volume 1 - Volume 1
Dynamic support vector machines for non-stationary time series forecasting

Intelligent Data Analysis

Quantified Score

Hi-index	0.00

Visualization

Abstract

Support vector machines (SVMs) have been successfully applied to solve nonlinear regression and times series problems. However, the application of SVMs for tourist forecasting has not been widely explored. Furthermore, most SVM models are applied for solving univariate forecasting problems. Therefore, this investigation examines the feasibility of SVMs with backpropagation neural networks in forecasting tourism demand influenced by different factors. A numerical example from an existing study is used to demonstrate the performance of tourist forecasting. Experimental results indicate that the proposed model outperforms other approaches for forecasting tourism demand.