Forecasting tourism demand by fuzzy time series models
International Journal of Culture, Tourism and Hospitality Research
ISSN: 1750-6182
Article publication date: 5 October 2012
Abstract
Purpose
This study aims to adapt a neural network based fuzzy time series model to improve Taiwan's tourism demand forecasting.
Design/methodology/approach
Fuzzy sets are for modeling imprecise data and neural networks are for establishing non‐linear relationships among fuzzy sets. A neural network based fuzzy time series model is adapted as the forecasting model. Both in‐sample estimation and out‐of‐sample forecasting are performed.
Findings
This study outperforms previous studies undertaken during the SARS events of 2002‐2003.
Research limitations/implications
The forecasting model only takes the observation of one previous time period into consideration. Subsequent studies can extend the model to consider previous time periods by establishing fuzzy relationships.
Originality/value
Non‐linear data is complicated to forecast, and it is even more difficult to forecast nonlinear data with shocks. The forecasting model in this study outperforms other studies in forecasting the nonlinear tourism demands during the SARS event of November 2002 to June 2003.
Keywords
Citation
Huarng, K., Hui‐Kuang Yu, T., Moutinho, L. and Wang, Y. (2012), "Forecasting tourism demand by fuzzy time series models", International Journal of Culture, Tourism and Hospitality Research, Vol. 6 No. 4, pp. 377-388. https://doi.org/10.1108/17506181211265095
Publisher
:Emerald Group Publishing Limited
Copyright © 2012, Emerald Group Publishing Limited