Forecasting Regional Tourism Demand in Morocco from Traditional and AI-Based Methods to Ensemble Modeling

El houssin Ouassou and Hafsa Taya
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El houssin Ouassou: Laboratory of Applied Economics (LAE), Mohammed V University of Rabat, Rabat 8007, Morocco
Hafsa Taya: Laboratory of Applied Economics (LAE), Mohammed V University of Rabat, Rabat 8007, Morocco

Forecasting, 2022, vol. 4, issue 2, 1-18

Abstract: Tourism is one of the main sources of wealth for the Moroccan regions, since, in 2019, it contributed 7.1% to the total GDP. However, it is considered to be one of the sectors most vulnerable to exogenous shocks (political and social stability, currency change, natural disasters, pandemics, etc.). To control this, policymakers tend to use various techniques to forecast tourism demand for making crucial decisions. In this study, we aimed to forecast the number of tourist arrivals to the Marrakech-Safi region using annual data for the period from 1999 to 2018 by using three conventional approaches (ARIMA, AR, and linear regression), and then we compared the results with three artificial intelligence-based techniques (SVR, XGBoost, and LSTM). Then, we developed hybrid models by combining both the conventional and AI-based models, using the technique of ensemble learning. The findings indicated that the hybrid models outperformed both conventional and AI-based techniques. It is clear from the results that using hybrid models can overcome the limitations of each method individually.

Keywords: regional tourism demand; forecasting; AI-based model; conventional model; hybrid model; ensemble learning (search for similar items in EconPapers)
JEL-codes: A1 B4 C0 C1 C2 C3 C4 C5 C8 M0 Q2 Q3 Q4 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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