Predicting International Tourist Arrivals in Namibia Using Machine Learning

Simple item page
dc.contributor.authorShivute, Selma
dc.date.accessioned2024-10-28T10:03:27Z
dc.date.available2024-10-28T10:03:27Z
dc.date.issued2024-08
dc.description.abstractThe 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.
dc.identifier.citationShivute. S. (2024). Predicting International Tourist Arrivals in Namibia Using Machine Learning [Master’s thesis, Namibia University of Science and Technology].
dc.identifier.urihttp://hdl.handle.net/10628/1040
dc.language.isoen
dc.subjectInternational tourist
dc.subjectMachine Learning
dc.subjectLearning models
dc.subjectPredicting
dc.titlePredicting International Tourist Arrivals in Namibia Using Machine Learning
dc.typeThesis

Files

Original bundle
Now showing 1 - 1 of 1
No Thumbnail Available
Name:
Selma Shivute_200517597_Thesis_Ounongo Repository.pdf
Size:
2.37 MB
Format:
Adobe Portable Document Format
Download
License bundle
Now showing 1 - 1 of 1
No Thumbnail Available
Name:
license.txt
Size:
1.71 KB
Format:
Item-specific license agreed upon to submission
Description:
Download

Collections

Masters and PhD Theses