Forecasting tourism demand by fuzzy time series models

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.

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