This study explores how to select the optimal number of lagged inputs (NLIs) in international tourism demand forecasting. With international tourist arrivals at 10 European countries, the performances of eight machine learning models are evaluated using different NLIs. The results show that: (1) as NLIs increases, the error of most machine learning models first decreases rapidly and then tends to be stable (or fluctuates around a certain value) when NLIs reaches a certain cutoff point. The cutoff point is related to 12 and its multiples. This trend is not affected by the size of the test set; (2) for nonlinear and ensemble models, it is better to select one cycle of the data as the NLIs, while for linear models, multiple cycles are a better choice; (3) significantly different prediction results are obtained by different categories of models when the optimal NLIs are used.

本研究探讨了如何在国际旅游需求预测中选择最佳滞后输入量（NLI）。随着国际游客到达10个欧洲国家，使用不同的NLI评估了8种机器学习模型的性能。结果表明：（1）随着NLI的增加，大多数机器学习模型的误差首先迅速减小，然后在NLI达到某个临界点时趋于稳定（或在某个值附近波动）。截止点与12及其倍数有关。该趋势不受测试集大小的影响；（2）对于非线性和整体模型，最好选择一个数据周期作为NLI，而对于线性模型，最好选择多个周期；