 Developing and testing the efficacy of a novel forecasting methodology: Theory and evidence from ChinaYuhong Yang, Tarik Dogru, Chao Liang, Jianqiong Wang and Pengfei Xu Tourism Economics, 2024, vol. 30, issue 8, 2043-2069 Abstract: Numerous methodologies have been offered to forecast tourism demand; however, accurate forecasting has been a major challenge for policymakers despite its critical importance for tourism planning. Therefore, we propose and test a novel forecasting methodology that combines principal component analysis (PCA) and long short-term memory (LSTM) network, along with the Baidu index, to forecast daily tourist arrivals for a popular tourist attraction in China. Word2Vec, a software tool launched by Google, is used to improve the coverage and accuracy of search keywords in the construction of the Baidu indexes. Before training the LSTM network, PCA is used to reduce noise and optimize the data. Considering the study’s timeframe, the impact of COVID-19 pandemic has also been assessed. The efficacy of the proposed forecasting methodology is verified, and the results show that the PCA-LSTM model outperforms other models in terms of prediction accuracy and stability. Theoretical and practical implications are discussed. Keywords: tourism demand forecasting; principal component analysis; long short-term memory network; Word2Vec; baidu index (search for similar items in EconPapers) Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:sae:toueco:v:30:y:2024:i:8:p:2043-2069 DOI: 10.1177/13548166241248866 Access Statistics for this article More articles in Tourism Economics |
Is your work missing from RePEc? Here is how to contribute.
Questions or problems? Check the EconPapers FAQ or send mail to .
EconPapers is hosted by the
School of Business at Örebro University.