Forecasting tourism demand based on improved fuzzy time series model

  • Authors:

  • Hung-Lieh Chou;Jr-Shian Chen;Ching-Hsue Cheng;Hia Jong Teoh

  • Affiliations:

  • Department of Information Management, National Yunlin University of Science and Technology, Touliu, Yunlin, Taiwan and Department of Computer Center, Huwei Township, Yunlin County, Taiwan;Department of Computer Science and Information Management, HUNGKUANG University, Shalu, Taichung, Taiwan;Department of Information Management, National Yunlin University of Science and Technology, Touliu, Yunlin, Taiwan;Department of Accounting and Information Technology, Ling Tung University, Nantun, Taichung, Taiwan

  • Venue:
  • ACIIDS'10 Proceedings of the Second international conference on Intelligent information and database systems: Part I
  • Year:
  • 2010

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Abstract

The total tourist arrivals, is an important factor to understand the tourism market and to predict the trend of tourism demand, is necessity and exigency for tourism demand and hospitality industries for subsequent planning and policy marketing. This paper proposed a fusion model of fuzzy time-series to improve the forecasting accuracy on total tourist arrivals, which consider the cluster characteristic of observations, define more persuasive universe of discourse based on k-mean approach, fuzzify the observation precisely by triangular fuzzy number, establish fuzzy logical relationships groups by employing rough set rule induction, and assign weight to various fuzzy relationship based on rule-support. In empirical case study, the proposed model is verified by using tourist datasets and comparing forecasting accuracy with listed models. The experimental results indicate that the proposed approach outperforms listed models with lower mean absolute percentage error.