Forecast combination with multivariate grey prediction for tourism demand forecasting

Article type: Research Article

Authors: Hu, Yi-Chung^{a; *} | Wu, Geng^b

Affiliations: [a] Department of Business Administration, | [b] School of Economics and Management, Ningbo University of Technology, Ningbo, China

Correspondence: [*] Corresponding author: Yi-Chung Hu, Department of Business Administration, Chung Yuan Christian University, Taoyuan City 320314, Taiwan. E-mail: [email protected].

Abstract: Empirical evidence has shown that forecast combination can improve the prediction accuracy of tourism demand forecasting. This paper aimed to develop a more accurate grey forecast combination method (GFCM) with multivariate grey prediction models In light of the practical applicability of grey prediction, which is not required to apply any statistical test to examine data series this research features the use of multivariate grey models through the genetic algorithm to synthesize forecasts from univariate grey prediction models commonly used in tourism forecasting into composite forecasts Empirical results showed that the proposed GFCM significantly outperformed the other combination methods considered. The results also suggested that the risk of forecast failures caused by selecting an inappropriate single model for tourism demand forecasting can be reduced by using the GFCM.

Keywords: Tourism demand, forecast combination, tourist arrivals, grey system, genetic algorithm

DOI: 10.3233/IDA-230565

Journal: Intelligent Data Analysis, vol. Pre-press, no. Pre-press, pp. 1-14, 2024