Can Google data improve the forecasting performance of tourist arrivals? Mixed-data sampling approach

Bangwayo-Skeete, Prosper; Skeete, Ryan W.

doi:10.1016/j.tourman.2014.07.014

Cited by 273 publications

(181 citation statements)

References 34 publications

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“…Although some existing studies have applied the Internet search index to the prediction of the number of tourists, the results showed that the average error was relatively high [3][4][5][6]. In this study, a feasible variable selection method named generalized dynamic factor model (GDFM) was proposed based on existing studies.…”

Section: Introductionmentioning

confidence: 99%

The Role of Internet Search Index for Tourist Volume Prediction Based on GDFM Model

Wang

et al. 2020

Teh. vjesn

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Tourist volume is increasing with the expansion of the scale of tourism, and improving the prediction of tourist volume is helpful for tourism managers to make decisions. Internet search index can be applied to predict the behavior of users, which is widely used in the study of tourist volume prediction and infectious disease prediction. However, the high dimension and correlation of Internet search index tends to reduce the accuracy of the models, which increases the average prediction error of common time-series models. The dynamic factor model (DFM) proposed in our study can be used to solve the problem. This study selects 23 variables and introduces the generalized dynamic factor model (GDFM) to predict tourist volume. The model cannot only reduce the dimensionality of high-dimensional Internet search index data, but also reflects the dynamic correlation between Internet search index data. The results show that the prediction accuracy is improved in our method, and the prediction accuracy of tourist volume is improved by over 10%, with an average error of only 4.3% when compared with the neural network (NN) model. Our study not only provides implications for decision-makers to predict tourist volume timely and accurately, but also helps companies understand tourist' behavior and make the best strategic decisions.

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Section: Introductionmentioning

confidence: 99%

The Role of Internet Search Index for Tourist Volume Prediction Based on GDFM Model

Wang

et al. 2020

Teh. vjesn

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“…Investigations of search data have been applied across a number of fields such as predicting changes in tourism numbers (Bangwayo‐Skeete and Skeete, ; Yang et al , ), offering an advance warning of flu epidemics (Ginsberg, ) or predicting exchange rate volatility (G. Smith, ).…”

Section: Introductionmentioning

confidence: 99%

Google's MIDAS Touch: Predicting UK Unemployment with Internet Search Data

Smith

2016

Journal of Forecasting

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Internet search data could be a useful source of information for policymakers when formulating decisions based on their understanding of the current economic environment. This paper builds on earlier literature via a structured value assessment of the data provided by Google Trends. This is done through two empirical exercises related to the forecasting of changes in UK unemployment. Firstly, economic intuition provides the basis for search term selection, with a resulting Google indicator tested alongside survey-based variables in a traditional forecasting environment. Secondly, this environment is expanded into a pseudo-time nowcasting framework which provides the backdrop for assessing the timing advantage that Google data have over surveys. The framework is underpinned by a MIDAS regression which allows, for the first time, the easy incorporation of Internet search data at its true sampling rate into a nowcast model for predicting unemployment.

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“…Most publications fall under the healthcare (see for example Ginsberg et al, 2009;Yang et al, 2011;Dehkordy et al, 2014) and economy (see for example Choi & Varian, 2009ª;Schmidt & Vosen, 2009;Baker & Fradkin, 2011;Bughin, 2015) fields, but work was also performed in finance (Smith, 2012), communication and marketing (Granka, 2010); religion (Scheitle, 2011); education (Vaughan-Frias et al, 2013), and cinema (Judge & Hand, 2010). The hospitality and tourism sector was analysed in the studies developed by Chamberlin, 2010;Choi and Varian, 2009b;Shimshoni et al, 2009;Suhoy, 2009;Smith and White, 2011;Artola and Galán, 2012;Gawlik et al, 2011;Saidi et al, 2010;Pan et al, 2012;Concha et al, 2015;Kallasidis, 2015;Bangwayo-Skeete and Skeete (2015); Dinis et al 2013Dinis et al , 2015); Jackman and Naitram, 2015); Yang et al (2015); Rivera (2016);and Li et al, 2017).…”

Section: Literature Reviewmentioning

confidence: 99%

Similarities and correlation between resident tourist overnights and Google Trends information in Portugal and its tourism regions

Dinis

Costa

Pacheco

2017

TMS

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Over the last years, we observed an exponential growth in the number of tourism consumers that use the Internet as a source of information during a destination selection process. Google Trends is a tool that displays data, almost in real time, on the interest of people in a particular topic based on search trends. This paper demonstrates that Google Trends is a tool that can provide useful and relevant information about the interests of individuals in relation to domestic tourism destinations at national and regional levels. Our findings indicate that overnights spent in hotel establishments by the residents in Portugal are strongly correlated with the Google index, mainly in mainland Portugal, Alentejo and Algarve regions, and that the results improve when more municipalities names and the national or the regional tourism brands are included as search terms.Keywords: Google Trends, domestic tourism, search data, Portugal, big data. ResumoAo longo dos últimos anos, tem-se verificado um crescimento exponencial no número de consumidores de turismo que utiliza a Internet como fonte de informação no processo de seleção do destino turístico. O Google Trends é uma ferramenta que disponibilizada dados, quase em tempo real, sobre o interesse das pessoas num determinado tema com base nas tendências de pesquisa. Este artigo demonstra que o Google Trends é uma ferramenta que disponibiliza informação útil e relevante sobre os interesses dos indivíduos em relação aos destinos de turismo doméstico ao nível nacional e regional. Os resultados indicam que as dormidas dos residentes em Portugal nos estabelecimentos hoteleiros estão fortemente correlacionadas com o índice do Google, principalmente em Portugal continental, na região do Alentejo e do Algarve, e que os resultados melhoram quando são incluídos como termos de pesquisa mais nomes de municípios e a marca de turismo nacional ou das regiões de turismo. Palavras-chave:Google Trends, turismo doméstico, dados de pesquisa, Portugal, big data.

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Can Google data improve the forecasting performance of tourist arrivals? Mixed-data sampling approach

Cited by 273 publications

References 34 publications

The Role of Internet Search Index for Tourist Volume Prediction Based on GDFM Model

The Role of Internet Search Index for Tourist Volume Prediction Based on GDFM Model

Google's MIDAS Touch: Predicting UK Unemployment with Internet Search Data

Similarities and correlation between resident tourist overnights and Google Trends information in Portugal and its tourism regions

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