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.

旅游需求预测在制定国家旅游发展政策中起着重要作用。有关旅游需求的可用数据通常由非线性实值序列组成。但是，样本通常来自不满足统计假设的不确定评估。因此，我们使用带有神经网络的模糊回归分析来生成由上，下环绕序列组成的数据间隔，以处理不确定性。然后，将这些数据间隔获得的最佳非模糊性能值应用于没有统计假设的优化灰色预测模型。利用国外游客的真实数据验证了所提出的区间灰色预测模型的预测精度。