Developing and testing the efficacy of a novel forecasting methodology: Theory and evidence from China
Year: 2024
Type: article
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.
Source: Tourism Economics
Authors: Yuhong Yang, Tarik Dogru, Chao Liang, Jianqiong Wang, Pengfei Xu
Institutions: Southwest Jiaotong University, Florida State University
Language: English
Field: Decision Sciences
Domain: Social Sciences
Open Access status: bronze
OpenAlex ID: W4394978232