Machine Learning based Forecasting Systems for Worldwide International Tourists Arrival

Abstract

The international tourist movement has overgrown in recent decades, and travelers are considered a significant source of income to the tourism economy. When tourists visit a place, they spend considerable money on their enjoyment, travel, and hotel accommodations. In this research, tourist data from 2010 to 2020 have been extracted and extended with depth analysis of different dimensions to identify valuable features. This research attempts to use machine learning regression techniques such as Support Vector Regression (SVR) and Random Forest Regression (RFR) to forecast and predict worldwide international tourist arrivals and achieved forecasting accuracy using SVR is 99.4% and using RFR is 84.7%. The study also analyzed the forecasting deadlock condition after covid-19 in the sudden drop of international visitors due to lockdown enforcement by all countries.

Advancing tourism demand forecasting in Sri Lanka: evaluating the performance of machine learning models and the impact of social media data integration

“…These models can potentially enhance the prediction accuracy of tourist arrivals by leveraging their ability to capture non-linear relationships and unveil hidden insights. Previous research has demonstrated the capacity of ML algorithms, such as artificial neural networks (ANNs) and support vector machines, to surpass conventional forecasting methods in prediction accuracy (Mishra et al, 2021). Moreover, the accessibility of rich social media data sources provides an opportunity to enhance predictions further by incorporating additional variables derived from online reviews and tourist discussions (Fronzetti Colladon et al, 2019;Li et al, 2020).…”

“…They suggest that forecasters in the tourism industry should only employ these two methods with serious consideration. Support vector machine (SVM) is applied for tourism demand analysis by (Mishra et al, 2021). SVM solves classification, non-linear regression estimation and prediction problems.…”

Social network analysis of tourism data: A case study of quarantine decisions in COVID-19 pandemic

“… Forster, Forster, Renfrew and Forster, 2020 , Knežević Cvelbar and Ogorevc, 2020 , Lenadora et al, 2020 , Martínez-López, Perez and Sánchez-Vizcaíno, 2009 , Mediouni, Madiouni and Kaczor-Urbanowiczc, 2020 , Mishra et al, 2021 , Palmer, 2016 , Sjödin et al, 2020 , Wiiava and Handoko, 2021 , Yang and Wong, 2020 …”

Efficient Function Integration and a Case Study with Gompertz Functions for Covid-19 Waves

“…Artificial intelligence and its machine learning methods have been applied in different areas and better results have been obtained than traditional methods such as the traditional regression with the normal equation [6]- [11]. In our work, a program has been developed to carry out regression using machine learning, as a case study the country of Peru was selected, which has been one of the countries with the highest mortality rate per inhabitant.…”