A deep learning approach for daily tourist flow forecasting with consumer search data. Issue 3 (3rd March 2020)

• Record Type: Journal Article
• Authors: Zhang, Binru; Li, Nao; Shi, Feng; Law, Rob
• Is Part Of: Asia Pacific journal of tourism research. Volume 25:Issue 3(2020)
• Page Start: 323
• Page End: 339
• Publication Date: 2020-03-03
• Subjects: Deep learning -- artificial intelligence -- LSTM network -- consumer search data -- big data -- relatively large sample -- tourism demand forecasting -- daily tourist flows -- long-term dependence -- forecasting precision; Tourism -- Asia -- Periodicals; Tourism -- Pacific Area -- Periodicals; 338.4791504429
• DOI: 10.1080/10941665.2019.1709876
• Languages: English
• ISSNs: 1094-1665

Abstract

ABSTRACT: This study introduces the concept of long short-term memory (LSTM) network to handle complex time series forecasting problems in the tourism industry. To validate the efficiency of the developed method, we used the daily tourist flow and consumer search data of Jiuzhaigou, a popular tourist spot in China, from 8 October 2013 to 7 August 2017 as the experimental dataset for empirical analysis. According to the 150-day forecasting results, LSTM shows the best statistical performance in the training and test sets compared with its counterparts.