Open AccessTourism Demand Modelling and Forecasting: A Review of Literature
Author(s) -
Musonera Abdou,
Edouard Musabanganji,
Herman Musahara
Publication year - 2021
Publication title -
african journal of hospitality, tourism and leisure
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.232
H-Index - 11
ISSN - 2223-814X
DOI - 10.46222/ajhtl.19770720.168
Subject(s) - demand forecasting , econometric model , tourism , popularity , computer science , benchmark (surveying) , technology forecasting , econometrics , predictive modelling , probabilistic forecasting , artificial intelligence , operations research , economics , machine learning , engineering , geography , psychology , social psychology , archaeology , geodesy , probabilistic logic
This research examines 145 key papers from 1979 to 2020 in order to gain a better sense of how tourism demand forecasting techniques have changed over time. The three types of forecasting models are econometric, time series, and artificial intelligence (AI) models. Econometric and time series models that were already popular in 2005 maintained their popularity, and were increasingly used as benchmark models for forecasting performance assessment and comparison with new models. In the last decade, AI models have advanced at an incredible rate, with hybrid AI models emerging as a new trend. In addition, some new developments in the three categories of models, such as mixed frequency, spatial regression, and combination and hybrid models have been introduced. The main conclusions drawn from historical comparisons forecasting methods are that forecasting models have become more diverse, that these models have been merged, and that forecasting accuracy has improved. Given the complexities of predicting tourism demand, there is no single approach that works well in all circumstances, and forecasting techniques are still evolving.