Search Engine Traffic as Input for Predicting Tourist Arrivals

Höpken, Wolfram; Eberle, Tobias; Fuchs, Matthias; Lexhagen, Maria

doi:10.1007/978-3-319-72923-7_29

Cited by 10 publications

(8 citation statements)

References 20 publications

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“…A main implication of this study is that online hotel room price data are beneficial as a basis for making daily tourism demand forecasts at a city level. The results of this research are consistent with the literature in other domains, which found improved predictions using online data sources (Gunter & Önder, ; Höpken et al, ; Höpken et al, ; Li et al, ; Önder & Gunter, ; Padhi & Pati, ). While previous works primarily considered search query data or derivatives thereof, this study shows that hotel room pricings in itself are a reliable source for forecasting tourism demand.…”

Section: Discussionsupporting

confidence: 91%

Gaussian processes for daily demand prediction in tourism planning

Tsang

Benoit

2020

Journal of Forecasting

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This study proposes Gaussian processes to forecast daily hotel occupancy at a city level. Unlike other studies in the tourism demand prediction literature, the hotel occupancy rate is predicted on a daily basis and 45 days ahead of time using online hotel room price data. A predictive framework is introduced that highlights feature extraction and selection of the independent variables. This approach shows that the dependence on internal hotel occupancy data can be removed by making use of a proxy measure for hotel occupancy rate at a city level. Six forecasting methods are investigated, including linear regression, autoregressive integrated moving average and recent machine learning methods. The results indicate that Gaussian processes offer the best tradeoff between accuracy and interpretation by providing prediction intervals in addition to point forecasts. It is shown how the proposed framework improves managerial decision making in tourism planning.

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Section: Discussionsupporting

confidence: 91%

Gaussian processes for daily demand prediction in tourism planning

Tsang

Benoit

2020

Journal of Forecasting

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“…Yet the predictive power of the Baidu index is also applied in Li et al's (2018) study, which confirms the previous studies in this area that demonstrate the predictive superiority of forecast models with Google Trends data over other forecast models (e.g. Höpken et al, 2018;Padhi and Pati, 2017). Pan and Yang (2017) apply website traffic data from the Google Analytics account of a destination website for forecasting weekly hotel occupancy and although it improves forecast accuracy, the improvement was not found to be substantial.…”

supporting

confidence: 82%

Exploring the predictive ability of LIKES of posts on the Facebook pages of four major city DMOs in Austria

2018

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Using data for the period 2010M06–2017M02, this study investigates the possibility of predicting total tourist arrivals to four Austrian cities (Graz, Innsbruck, Salzburg, and Vienna) from LIKES of posts on the Facebook pages of the destination management organizations of these cities. Google Trends data are also incorporated in investigating whether forecast models with LIKES and/or with Google Trends deliver more accurate forecasts. To capture the dynamics in the data, the autoregressive distributed lag (ADL) model class is employed. Taking into account the daily frequency of the original LIKES data, the mixed data sampling (MIDAS) model class is employed as well. While time-series benchmarks from the naive, error–trend–seasonal, and autoregressive moving average model classes perform best for Graz and Innsbruck across forecast horizons and forecast accuracy measures, ADL models incorporating only LIKES or both LIKES and Google Trends generally outperform their competitors for Salzburg. For Vienna, the MIDAS model including both LIKES and Google Trends produces the smallest forecast accuracy measure values for most forecast horizons.

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“…Analogous to Bangwayo-Skeete and Skeete (2015), X. Yang et al (2015), Önder and Gunter (2016), and Höpken et al (2018), search traffic on the Google search engine has been selected, as Google is the predominant search engine in most sending countries (c.f. Pearson CMG 2017).…”

Section: Methodsmentioning

confidence: 99%

“…In recent literature, big data have already been used for tourism demand predictions (Y. Yang, Pan, and Song 2014; Önder and Gunter 2016; Höpken et al 2017b; Höpken et al 2018; Y. Liu, Tseng, and Tseng 2018).…”

Section: Introductionmentioning

confidence: 99%

Improving Tourist Arrival Prediction: A Big Data and Artificial Neural Network Approach

Höpken

Eberle

Fuchs

et al. 2020

Journal of Travel Research

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103

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Because of high fluctuations of tourism demand, accurate predictions of tourist arrivals are of high importance for tourism organizations. The study at hand presents an approach to enhance autoregressive prediction models by including travelers’ web search traffic as external input attribute for tourist arrival prediction. The study proposes a novel method to identify relevant search terms and to aggregate them into a compound web-search index, used as additional input of an autoregressive prediction approach. As methods to predict tourism arrivals, the study compares autoregressive integrated moving average (ARIMA) models with the machine learning–based technique artificial neural network (ANN). Study results show that (1) Google Trends data, mirroring traveler’s online search behavior (i.e., big data information source), significantly increase the performance of tourist arrival prediction compared to autoregressive approaches using past arrivals alone, and (2) the machine learning technique ANN has the capacity to outperform ARIMA models.

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Search Engine Traffic as Input for Predicting Tourist Arrivals

Cited by 10 publications

References 20 publications

Gaussian processes for daily demand prediction in tourism planning

Gaussian processes for daily demand prediction in tourism planning

Exploring the predictive ability of LIKES of posts on the Facebook pages of four major city DMOs in Austria

Improving Tourist Arrival Prediction: A Big Data and Artificial Neural Network Approach

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