Predicting International Tourist Arrivals in Namibia Using Machine Learning
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Date
2024-08
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Abstract
The lack of accurate and timely predictions for international tourist arrivals in Namibia remains
an open problem, leading to inefficiencies in tourism planning and resource allocation. Traditional
methods, primarily based on seasonal trends and historical data, dominate the forecasting
landscape. Although traditional approaches are good at capturing seasonal patterns, there is always
a lack of accounting for more dynamic and non-linear interactions between the predictive variables
and more reliable results.
In recent years, machine learning models like Seasonal Autoregressive Integrated Moving Average
(SARIMA), Random Forest, and Prophet have gained increasing support for their ability to handle
complex, non-linear data and provide more accurate tourist arrival forecasts. SARIMA is
particularly good at modelling seasonal time series data, Random Forest excels in capturing nonlinear relationships, and Prophet is designed to handle time series data as well as irregular and
missing data. However, attempts to implement these three models in predicting international
tourist arrivals in the Namibian context have exposed limitations such as a constant prediction with
Random Forest and the need for extensive tuning in SARIMA and Prophet, which may result in
their prediction accuracy showing little improvements in Namibia’s tourism sector.
This study aimed to develop and test the three models: SARIMA, Random Forest, and Prophet, to
predict international tourist arrivals in Namibia more accurately. Accurate forecasts can improve
decision-making within the tourism sector, infrastructure planning and resource allocation. The
methodology used involved data preparation and data exploratory strategies to determine the
relationship between exploratory variables and dependent variables. It also included training and
validating these models on historical data obtained from the Ministry of Home Affairs,
Immigration, Safety and Security. The models were hyper-tuned to overcome the limitations of
accuracy by improving accurate predictions.
It is expected that these models will overcome the limitations of non-accuracy predictions of tourist
arrivals. The effectiveness of these models' accuracy was evaluated using Root Mean Square Error
and Mean Absolute Error and comparing their performance against each other to determine the
preferred model for Namibia. The results indicated that SARIMA achieved the most accurate
prediction, followed by Random Forest, and Prophet performed poorly in predicting international
visitor arrivals in Namibia. The two models are anticipated to contribute significantly to more
efficient tourism management and planning in Namibia.
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Keywords
International tourist, Machine Learning, Learning models, Predicting
Citation
Shivute. S. (2024). Predicting International Tourist Arrivals in Namibia Using Machine Learning [Master’s thesis, Namibia University of Science and Technology].