Machine Learning based Forecasting Systems for Worldwide International Tourists Arrival

Journal article


Mishra, Ram Krishn, Urolagin, Siddhaling, Jothi, J. Angel Arul, Nawaz, Nishad and Haywantee, Ramkissoon 2021. Machine Learning based Forecasting Systems for Worldwide International Tourists Arrival. International Journal of Advanced Computer Science and Applications. https://doi.org/10.14569/IJACSA.2021.0121107
AuthorsMishra, Ram Krishn, Urolagin, Siddhaling, Jothi, J. Angel Arul, Nawaz, Nishad and Haywantee, Ramkissoon
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.

KeywordsTourists; forecasting; machine learning; Covid-19
Year2021
JournalInternational Journal of Advanced Computer Science and Applications
PublisherThe Science and Information Organisation
ISSN2156-5570
Digital Object Identifier (DOI)https://doi.org/10.14569/IJACSA.2021.0121107
Web address (URL)http://hdl.handle.net/10545/626153
hdl:10545/626153
Publication datesNov 2021
Publication process dates
Deposited13 Dec 2021, 12:17
AcceptedNov 2021
ContributorsBITS Pilani, Dubai Campus Dubai, United Arab Emirates, Kingdom University, Riffa, Kingdom of Bahrain and University of Derby
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