International tourism demand forecasting with machine learning models: The power of the number of lagged inputs

Jian-Wu Bi, Tian-Yu Han and Hui Li
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Jian-Wu Bi: Nankai University, China
Tian-Yu Han: Northeastern University, China
Hui Li: Nankai University, China

Tourism Economics, 2022, vol. 28, issue 3, 621-645

Abstract: 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.

Keywords: experimental study; machine learning models; number of lagged inputs; tourism demand forecasting (search for similar items in EconPapers)
Date: 2022
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Citations: View citations in EconPapers (2)

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Persistent link: https://EconPapers.repec.org/RePEc:sae:toueco:v:28:y:2022:i:3:p:621-645

DOI: 10.1177/1354816620976954

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