Multi-horizon accommodation demand forecasting: A New Zealand case study

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.

Automated summary

This paper fills two gaps in accommodation demand forecasting: the limited number of studies on using modern machine learning techniques, and the lack of understanding of comparative forecasting performance at multiple forecast horizons. Machine learning performance is stable and robust as the forecast horizon increases, with long short-term memory showing particular advantages in long-horizon forecasting and dealing with complex data structures in New Zealand.