Abstract
This study uses machine learning, including Support Vector Machines, Decision Trees, K-Nearest Neighbors, to examine Bangladesh’s tourism industry to forecast traveller preferences. We use time series analysis, including ARIMA, Moving Average, and Auto-regression models, to predict future tourism trends. Our results show that, with an accuracy of 96.3%, Linear SVM was the best at predicting preferences. For trend forecasting, the ARIMA model fared better than the others, suggesting that Bangladeshi tourism may be headed in an undesirable direction. Our observations and insights can help guide strategic choices and decisions in the creation of policies and the administration of tourism.
| Original language | English |
|---|---|
| Article number | 2415568 |
| Number of pages | 32 |
| Journal | Enterprise Information Systems |
| Volume | 18 |
| Issue number | 12 |
| Early online date | 4 Nov 2024 |
| DOIs | |
| Publication status | Published - 4 Nov 2024 |
Bibliographical note
Copyright © 2024 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group.This is an Open Access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. The terms on which this article has been published allow the posting of the Accepted Manuscript in a repository by the author(s) or with their consent.
Keywords
- ARIMA model
- Machine learning
- bangladesh tourism
- support vector machine
- time series analysis
- tourism analytics