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Forecasting Chinese tourist volume with search engine data · Study

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ArticleForecasting Chinese tourist volume with search engine data

Xin Yang2014

Article2014

Forecasting Chinese tourist volume with search engine data

Xin Yang · Bing Pan · James A. Evans · Benfu Lv

432 citations·0/0 files stored·Tourism Management

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Forecasting Techniques and Applications Diverse Aspects of Tourism Research Information Retrieval and Search Behavior

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References

2012Predicting the Present with Google Trends 2007Tourism demand modelling and forecasting—A review of recent research 2011The impact of financial development on carbon emissions: An empirical analysis in China 2011Forecasting private consumption: survey-based indicators vs. Google trends 2009Google Econometrics and Unemployment Forecasting

Cited by

2016A Big Data Analytics Method for Tourist Behaviour Analysis 2018Forecasting tourist arrivals with machine learning and internet search index 2016Forecasting tourism demand with composite search index 2017New developments in tourism and hotel demand modeling and forecasting 2020Forecasting tourism demand with multisource big data 2018Effective tourist volume forecasting supported by PCA and improved BPNN using Baidu index 2020Bayesian BILSTM approach for tourism demand forecasting 2016Forecasting Destination Weekly Hotel Occupancy with Big Data

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Papers that cite the same references — the relational fallback while embeddings are still backfilling.

2018Forecasting tourist arrivals with machine learning and internet search indexarticle · 4 shared references 2016Forecasting tourism demand with composite search indexarticle · 4 shared references 2014Can Google data improve the forecasting performance of tourist arrivals? Mixed-data sampling approacharticle · 4 shared references 2018Effective tourist volume forecasting supported by PCA and improved BPNN using Baidu indexarticle · 4 shared references 2016Short-term forecasting of Japanese tourist inflow to South Korea using Google trends dataarticle · 4 shared references 2019Spurious patterns in Google Trends data - An analysis of the effects on tourism demand forecasting in Germanyarticle · 4 shared references

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Year2014

Typearticle

Published2014-08-20

VenueTourism Management

ISSN-L0261-5177

Languageen

Citations432

Files0 public / 0

ShelfForecasting Techniques and Applications

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doi10.1016/j.tourman.2014.07.019

openalex_workW2086074129

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