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Fuzzified grey prediction models using neural networks for tourism demand forecasting

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Abstract

Tourism demand forecasting plays a significant role in devising tourism development policies for countries. Available data on tourism demand usually consist of a nonlinear real-valued sequence. However, the samples are often derived from uncertain assessments that do not satisfy statistical assumptions. Therefore, we use fuzzy regression analysis with neural networks to generate data intervals consisting of upper and lower wrapping sequences to deal with uncertainty. Then, the best non-fuzzy performance values obtained by these data intervals are applied to optimize grey prediction models without statistical assumptions. The forecasting accuracy of the proposed interval grey prediction models was verified using real data on foreign tourists. The results show that the proposed prediction models are comparable to the other interval grey prediction models considered.

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Acknowledgements

The authors would like to thank the anonymous referees for their valuable comments. This research is supported by the Ministry of Science and Technology, Taiwan under Grant MOST 108-2410-H-033-038-MY2.

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Authors and Affiliations

  1. College of Management and College of Tourism, Fujian Agriculture and Forestry University, Fuzhou City, China

    Yi-Chung Hu

  2. Department of Business Administration, Chung Yuan Christian University, Taoyuan City, Taiwan

    Yi-Chung Hu

  3. School of Business, Shandong University, Weihai City, China

    Peng Jiang

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  1. Yi-Chung Hu
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  2. Peng Jiang
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Correspondence to Peng Jiang.

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Hu, YC., Jiang, P. Fuzzified grey prediction models using neural networks for tourism demand forecasting. Comp. Appl. Math. 39, 145 (2020). https://doi.org/10.1007/s40314-020-01188-6

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