Fuzzified grey prediction models using neural networks for tourism demand forecasting
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Fuzzified grey prediction models using neural networks for tourism demand forecasting Yi-Chung Hu1,2
· Peng Jiang3
Received: 16 February 2020 / Revised: 4 April 2020 / Accepted: 6 May 2020 © SBMAC - Sociedade Brasileira de Matemática Aplicada e Computacional 2020
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. Keywords Neural network · Fuzzy regression · Grey prediction · Artificial intelligence · Tourism demand Mathematics Subject Classification 00A69
1 Introduction Accurate demand forecasts are the foundation upon which tourism-related business decisions depend (Wu et al. 2017). To promote the tourism industry, the effectiveness of forecasting tourism demand can have a significant impact on investment decisions of private-sector actors and government (Lin et al. 2011). The most widely used measure of tourism demand is the number of tourists arriving at a destination (Habibi et al. 2009; Ouerfelli 2008). However, the
Communicated by Marcos Eduardo Valle.
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Peng Jiang [email protected]
1
College of Management and College of Tourism, Fujian Agriculture and Forestry University, Fuzhou City, China
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Department of Business Administration, Chung Yuan Christian University, Taoyuan City, Taiwan
3
School of Business, Shandong University, Weihai City, China 0123456789().: V,-vol
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variety of international tourism markets has rendered it challenging to predict the number of foreign tourists (Sun et al. 2016). Many forecasting models have been used for tourism demand prediction, including time series models (e.g., Assaf et al. 2011; Beneki et al. 2012; Claveria and Torra 2014; Claveria and Datzira 2010; Tsui et al. 2014; Yu and Schwartz 2006), econometric methods (e.g., Li et al. 2013; Onafowora and Owoye 2012; Song et al. 2012), and neural networks (NNs) (e.g., Cang 2014; Cuhadar et al. 2014; Claveria et al. 2015; Claveria and Torra 2014; Lin et al. 2011). However, a relatively large number of samples are required for statistical regression and time series analysis in the related contexts (Liu et al. 2017; Wang and Hsu 2008). Moreover, econometric methods and NNs are adversely affected by incomplete information associated with explanatory f
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