Please use this identifier to cite or link to this item: http://cmuir.cmu.ac.th/jspui/handle/6653943832/72785
Title: Application of Machine Learning Concept to Tourism Demand Forecast
Authors: Nachatpong Kaewsompong
Sukrit Thongkairat
Paravee Maneejuk
Keywords: Computer Science
Issue Date: 1-Jan-2022
Abstract: This study aims to examine the predictability of Support Vector Machine (SVM) and deep learning methods, which are Artificial Neural Network (ANN), and Recurrent Neural Network (RNN), and long short-term memory (LSTM). Tourism’s motivation reflected by the Search Intensity Indices (SII) is considered as the exogenous input in the forecasting model. Several forecasting criteria are implemented to measure the performance of these forecasting models. The models are applied to forecast the tourism demand, and the empirical results demonstrate that the deep learning approach, especially LSTM, seems to outperform the other learning models as well as the traditional regression model. Additionally, the forecasting performance of the forecasting models could be improved if the tourism’s motivation variable is augmented as the leading indicator in the model.
URI: https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85113402554&origin=inward
http://cmuir.cmu.ac.th/jspui/handle/6653943832/72785
ISSN: 18609503
1860949X
Appears in Collections:CMUL: Journal Articles

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