A deep learning approach for daily tourist flow forecasting with consumer search data

Binru Zhang; Nao Li; Feng Shi; Rob Law

doi:10.1080/10941665.2019.1709876

A deep learning approach for daily tourist flow forecasting with consumer search data

Binru Zhang, Nao Li, Feng Shi, Rob Law

Research output: Journal article publication › Journal article › Academic research › peer-review

56 Citations (Scopus)

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.

Original language	English
Pages (from-to)	323-339
Number of pages	17
Journal	Asia Pacific Journal of Tourism Research
Volume	25
Issue number	3
DOIs	https://doi.org/10.1080/10941665.2019.1709876
Publication status	Published - 3 Mar 2020

Keywords

artificial intelligence
big data
consumer search data
daily tourist flows
Deep learning
forecasting precision
long-term dependence
LSTM network
relatively large sample
tourism demand forecasting

ASJC Scopus subject areas

Geography, Planning and Development
Tourism, Leisure and Hospitality Management

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10.1080/10941665.2019.1709876

Cite this

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title = "A deep learning approach for daily tourist flow forecasting with consumer search data",

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.",

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AB - 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.

KW - artificial intelligence

KW - big data

KW - consumer search data

KW - daily tourist flows

KW - Deep learning

KW - forecasting precision

KW - long-term dependence

KW - LSTM network

KW - relatively large sample

KW - tourism demand forecasting

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JO - Asia Pacific Journal of Tourism Research

JF - Asia Pacific Journal of Tourism Research

IS - 3

ER -

A deep learning approach for daily tourist flow forecasting with consumer search data

Abstract

Keywords

ASJC Scopus subject areas

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