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Multi‐horizon accommodation demand forecasting: A New Zealand case study
Author(s) -
Zhu Min,
Wu Jinran,
Wang YouGan
Publication year - 2020
Publication title -
international journal of tourism research
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.155
H-Index - 58
eISSN - 1522-1970
pISSN - 1099-2340
DOI - 10.1002/jtr.2416
Subject(s) - accommodation , horizon , demand forecasting , computer science , econometrics , term (time) , economics , operations research , engineering , operations management , mathematics , psychology , physics , geometry , quantum mechanics , neuroscience
Abstract This paper contributes to the filling of two gaps in accommodation demand forecasting: (a) the limited number of studies on the use of modern machine learning techniques to identify the dynamics of accommodation demand; and (b) the lack of understanding of comparative forecasting performance of different modelling techniques at multiple forecast horizons. We show that, as the forecast horizon increases, the performance of machine learning is stable and robust. We also find that the long short‐term memory has particular advantages in long‐horizon forecasting and handling data with complex structure in New Zealand.