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.

中文翻译：

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多水平住宿需求预测：新西兰案例研究

本文有助于填补住宿需求预测中的两个空白：（a）关于使用现代机器学习技术来识别住宿需求动态的研究数量有限；（b）缺乏对多种预测技术下不同建模技术的比较预测性能的了解。我们表明，随着预测范围的增加，机器学习的性能是稳定且强大的。我们还发现，在新西兰，长期短期记忆在长期预测和处理结构复杂的数据方面具有特殊优势。