Improved tourism demand forecasting with CIR# model: a case study of disrupted data patterns in Italy

Tourism Review. 2024. Vol. 79. No. 2. P. 445–464.

Bufalo M., Орландо Д.

This study aims to predict overnight stays in Italy at tourist accommodation facilities through a nonlinear, single factor, stochastic model called CIR#. The contribution of this study is twofold: in terms of forecast accuracy and in terms of parsimony (both from the perspective of the data and the complexity of the modeling), especially when a regular pattern in the time series is disrupted. This study shows that the CIR# not only performs better than the considered baseline models but also has a much lower error than other additional models or approaches reported in the literature.

Ключевые слова: time series forecasting, Tourism, COVID-19, Holt–Winters, CIR#, DNNAR