Article: A feature-based model selection approach using web traffic for tourism data Journal: International Journal of Web and Grid Services (IJWGS) 2024 Vol.20 No.3 pp.342 - 359 Abstract: The increased volume of accessible internet data creates an opportunity for researchers and practitioners to improve time series forecasting for many indicators. In our study, we assess the value of web traffic data in forecasting the number of short-term visitors travelling to Australia. We propose a feature-based model selection framework which combines random forest with feature ranking process to select the best performing model using limited and informative number of features extracted from web traffic data. The data was obtained for several tourist attraction and tourism information websites that could be visited by potential tourists to find out more about their destinations. The results of random forest models were evaluated over 3- and 12-month forecasting horizon. Features from web traffic data appears in the final model for short term forecasting. Further, the model with additional data performs better on unseen data post the COVID19 pandemic. Our study shows that web traffic data adds value to tourism forecasting and can assist tourist destination site managers and decision makers in forming timely decisions to prepare for changes in tourism demand. Inderscience Publishers - linking academia, business and industry through research

Title: A feature-based model selection approach using web traffic for tourism data

Authors: Ali Abdul Karim; Eric Pardede; Scott Mann

Addresses: Department of Computer Science and Information Technology, La Trobe University, Melbourne, 3083, Australia ' Department of Computer Science and Information Technology, La Trobe University, Melbourne, 3083, Australia ' Department of Computer Science and Information Technology, La Trobe University, Melbourne, 3083, Australia

Abstract: The increased volume of accessible internet data creates an opportunity for researchers and practitioners to improve time series forecasting for many indicators. In our study, we assess the value of web traffic data in forecasting the number of short-term visitors travelling to Australia. We propose a feature-based model selection framework which combines random forest with feature ranking process to select the best performing model using limited and informative number of features extracted from web traffic data. The data was obtained for several tourist attraction and tourism information websites that could be visited by potential tourists to find out more about their destinations. The results of random forest models were evaluated over 3- and 12-month forecasting horizon. Features from web traffic data appears in the final model for short term forecasting. Further, the model with additional data performs better on unseen data post the COVID19 pandemic. Our study shows that web traffic data adds value to tourism forecasting and can assist tourist destination site managers and decision makers in forming timely decisions to prepare for changes in tourism demand.

Keywords: tourism demand forecasting; web traffic data; random forest; feature ranking; time series forecasting.

DOI: 10.1504/IJWGS.2024.139786

International Journal of Web and Grid Services, 2024 Vol.20 No.3, pp.342 - 359

Received: 20 Jun 2023
Accepted: 27 Mar 2024
Published online: 05 Jul 2024 *

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Title: A feature-based model selection approach using web traffic for tourism data

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