Somasiri, Nalinda ORCID: https://orcid.org/0000-0001-6311-2251, Karunarathne, Lakmali ORCID: https://orcid.org/0009-0000-7720-7817 and Ganesan, Swathi ORCID: https://orcid.org/0000-0002-6278-2090 (2024) Predictive Modeling of Tourist Arrivals in Sri Lanka Using Linear Regression. The 1st International Conference on Advanced Computing Technologies (ICACT 2024), 1 (1). pp. 52-62.

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Abstract

One of the sectors of a developing nation's economy that directly generates income is tourism. For this reason, predicting the number of visitors is crucial when deciding on policies to upgrade facilities and other relevant aspects of this sector. The data for this paper were gathered from the Corporate website of the Sri Lanka tourism development authority(SLTDA)This paper attempts to forecast tourist arrivals in Sri Lanka using Linear Regression Model. The time span used for this study is from January 2021 to March 2024. The performance of the model was evaluated using metrics such as Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE) and R2 score. This paper forecasts tourist arrivals in Sri Lanka using the Linear Regression Model. While Linear Regression is suitable due to its simplicity and the scope of the dataset, more advanced forecasting models such as ARIMA or deep learning approaches like LSTM could have been considered for a more robust comparison. The study uses data from January 2021 to March 2024, and the model's performance is evaluated using metrics such as Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and R² score. The findings suggest that machine learning models can improve the forecasting accuracy of visitor arrivals, providing valuable insights for stakeholders in the tourism industry.

Keywords: Linear Regression Model, forecast, tourist arrivals, predictions, MAE, RMSE, R2 score

Item Type:	Article
Status:	Published
Subjects:	Q Science > Q Science (General) > Q325 Machine learning
School/Department:	London Campus
URI:	https://ray.yorksj.ac.uk/id/eprint/12702

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