<i>Research Note:</i> Nowcasting Tourist Arrivals in Barbados – Just Google it!

Jackman, Mahalia; Naitram, Simon

doi:10.5367/te.2014.0402

Cited by 32 publications

(25 citation statements)

References 10 publications

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“…Volchek et al (2019) compare mixed-frequency data sampling with ANN models and claim that higher frequency search data enhance the performance of models used to predict tourism demand. In contrast, Jackman and Naitram (2015) find no evidence suggesting that Google Trends data add any significant information to the models. The authors use support vector regressions and Google search data to test whether Google Trends can help predict tourist arrivals in Barbados.…”

Section: Literature Reviewmentioning

confidence: 69%

Forecasting tourist arrivals: Google Trends meets mixed-frequency data

Havránek

Zeynalov

2019

Tourism Economics

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In this article, we examine the usefulness of Google Trends data in predicting monthly tourist arrivals and overnight stays in Prague during the period between January 2010 and December 2016. We offer two contributions. First, we analyze whether Google Trends provides significant forecasting improvements over models without search data. Second, we assess whether a high-frequency variable (weekly Google Trends) is more useful for accurate forecasting than a low-frequency variable (monthly tourist arrivals) using mixed-data sampling (MIDAS). Our results suggest the potential of Google Trends to offer more accurate predictions in the context of tourism: we find that Google Trends information, both 2 months and 1 week ahead of arrivals, is useful for predicting the actual number of tourist arrivals. The MIDAS forecasting model employing weekly Google Trends data outperforms models using monthly Google Trends data and models without Google Trends data.

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Section: Literature Reviewmentioning

confidence: 69%

Forecasting tourist arrivals: Google Trends meets mixed-frequency data

Havránek

Zeynalov

2019

Tourism Economics

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“…In other words, the fit between the estimation model and the prediction model is good. Next, the forecasting ability was tested based on two forecasting accuracy criteria: RMSE and MAPE (Jackman & Naitram, ; Li et al, ). Table shows the predictive accuracy evaluation.…”

Section: Resultsmentioning

confidence: 99%

“…Li et al () empirically tested a framework for predicting tourist numbers to Beijing by incorporating search‐trend data and found similar results. Jackman and Naitram () explored support vector regressions and Google search data to test whether observing people's Internet habits could provide insights into trends in tourist arrivals to Barbados. Their findings revealed that Google Trends data could be used to identify changing patterns and trends in tourist arrivals from the UK and Canada.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Some recent studies have explored means of improving demand‐forecasting models for both the tourism industry (e.g., Cang, ; Park, Lee, & Song, ) and hospitality industry (e.g., Tang, King, & Pratt, ), giving special focus to those sectors. At the same time, several recent studies have developed demand‐forecasting models employing Internet search data, with Google Trends data being incorporated into tourism‐studies projects attempting to forecast the number of tourists heading to specific destinations (Bangwayo‐Skeete & Skeete, ; Jackman & Naitram, ; Li et al, ; Pan, Wu, & Song, ; Park et al, ; Yang, Pan, Evans, & Lv, ). Yet, although this existing tourism research has begun drawing from Google's data sources to forecast tourist numbers for destinations, still little research into casino‐demand forecasting based on Internet‐search data, such as that now being made available by Google Trends, has been attempted.…”

Section: Introductionmentioning

confidence: 99%

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Forecasting casino revenue by incorporating Google trends

Kim

Malek

2018

Journal of Tourism Research

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This study investigates whether Google Trends has predictive power for improving casinorevenue forecasting. More specifically, the present study aims to (a) examine the value of using readily available Google Trends data to improve predictive accuracy in the forecasting of casino revenue and (b) compare domestic casinos and foreigner-only casinos given the significantly different spending patterns of such casinos' patrons. Accordingly, this research utilizes time-series analyses that incorporate Google Trend data into five steps: the selection of keywords, the processing of search data, indexing construction, the prediction casino revenues, and evaluations of forecast accuracy. The evidence collected suggests that forecasting models using Google Trends data do significantly improve forecasting models for casino revenue. This study provides discussions of and implications for this type of methodology, for casino research, and for better industry practices.

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“…Support Vector Regressions were used in Jackman and Naitram (2016) to determine if Google search data can act as an indicator for trends in tourist arrivals in Barbados. The authors find that whilst Google Trends data can pick up significant information pertaining to tourist arrivals from UK and Canada there is no evidence in terms of tourist arrivals from US.…”

Section: Literature Reviewmentioning

confidence: 99%

Cross country relations in European tourist arrivals

Silva¹,

Ghodsi²,

Ghodsi³

et al. 2017

Annals of Tourism Research

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This paper introduces an optimized Multivariate Singular Spectrum Analysis (MSSA) algorithm for identifying leading indicators. Exploiting European tourist arrivals data, we analyse cross country relations for European tourism demand. Cross country relations have the potential to aid in planning and resource allocations for future tourism demand by taking into consideration the variation in tourist arrivals across other countries in Europe. Our findings indicate with statistically significant evidence that there exists cross country relations between European tourist arrivals which can help in improving the predictive accuracy of tourism demand. We also find that MSSA has the capability of not only identifying leading indicators, but also forecasting tourism demand with far better accuracy in comparison to its univariate counterpart, Singular Spectrum Analysis.

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Research Note: Nowcasting Tourist Arrivals in Barbados – Just Google it!

Cited by 32 publications

References 10 publications

Forecasting tourist arrivals: Google Trends meets mixed-frequency data

Forecasting tourist arrivals: Google Trends meets mixed-frequency data

Forecasting casino revenue by incorporating Google trends

Cross country relations in European tourist arrivals

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