This document presents research on using big data and data mining techniques to improve predictions of tourist arrivals. The researchers collected data on past tourist arrivals in Sweden as well as various potential predictor variables, including economic factors, web search traffic, and advertising expenditures. They used linear regression and k-nearest neighbors (k-NN) models to predict arrivals. The results showed that including big data sources like web search traffic improved predictions over traditional autoregressive models. Additionally, the k-NN data mining technique produced more accurate predictions than the linear regression statistical approach. The researchers conclude that big data and data mining are promising avenues for enhancing tourism demand forecasting.

1. ENTER 2017 Research Track Slide Number 1
Wolfram Höpkena, Dominic Ernestia, Matthias Fuchsb,
Kai Kronenbergb, and Maria Lexhagenb
a Business Informatics Group
University of Applied Sciences Ravensburg-Weingarten, Germany
{name.surname}@hs-weingarten.de
b European Tourism Research Institute (ETOUR)
Mid-Sweden University, Sweden
{name.surname}@miun.se
Big data as input for
predicting tourist arrivals

2. ENTER 2017 Research Track Slide Number 2
Content
• Introduction
• Related work
• Data collection and preparation
• Model building
• Results
• Conclusion and Outlook

3. ENTER 2017 Research Track Slide Number 3
Motivation
• Demand prediction in tourism
– Being a labour-intensive branch, tourism managers strongly depend on
precise demand predictions
– Besides long-term trends, tourist arrivals typically follow strong
seasonal trends
Autoregressive approaches lead to quite satisfactory results and have
been used quite widely in tourism in the past (Song & Li, 2008, p. 210)
• Limitations of autoregressive approaches
– Tourism demand is heavily influenced by external factors as well
• Economic factors: exchange rates, consumers’ income (Frechtling, 2002; Song & Witt, 2000)
• Destination-specific factors: marketing efforts (Brida & Schubert, 2008; Kronenberg et al. 2016)
– Electronic footprints of customers are promising new info sources
• Especially to encounter singular or unexpected demand fluctuations
induced by, e.g. natural disasters or mega events

4. ENTER 2017 Research Track Slide Number 4
Objective
• Research proposition (1): Big data information sources
increase performance of predicting tourist arrivals compared
to autoregressive approaches
– Extend autoregressive demand prediction by making use of big data
information sources
• Economic factors
• Data on web search behaviour of potential customers
• Research proposition (2): data mining technique can achieve
better prediction performance than statistical approaches
(e.g. linear regression)
– Data mining technique k-nearest neighbour
• Not restricted to a linear relationships, robust against biased and
statistically inconvenient data (e.g. collinearity of input attributes)

5. ENTER 2017 Research Track Slide Number 5
Content
• Introduction
• Related work
• Data collection and preparation
• Model building
• Results
• Conclusion and Outlook

6. ENTER 2017 Research Track Slide Number 6
Related work
• (Non-causal) Time series models
– Autoregressive moving average (ARIMA) models,
exponential smoothing models (Song & Li, 2008, p. 210)
• (Causal) Econometric approaches
– Autoregressive distributed lag models (ADLM), error correction model
(EDM), vector autoregressive (VAR) model, time varying parameter
(TVP) model to avoid spurious regression results (Peng et al. 2010)
– Linear structural equation model (SEM) (Turner & Witt 2001)
– Important determinants of tourism demand
• Consumer price index, GDP, exchange rates, interest rate, unemployment
rate, money supply, export/import rates (Cho 2001; Song & Li 2008, p. 211)
• Man-made events, advertising investments, crises (e.g. financial crisis,
terror attacks), natural disasters (SARS, foot & mouth disease, etc.)

7. ENTER 2017 Research Track Slide Number 7
Related work
• Using big data sources to predict tourism demand
– Google search engine traffic to increase forecasting performance using
autoregressive mixed-data sampling (AR-MIDAS) models
(Bangwayo-Skeete & Skeete, 2015)
– Google search engine traffic to improve tourism demand prediction
accuracy, compared to purely autoregressive models or exponential
smoothing time-series models (Önder & Gunter, 2016)
– Search engine data to increase forecasting accuracy, compared to
autoregressive moving average (ARMA) models (Yang et al., 2015)
– DMO Web traffic data to predict demand for hotel rooms in a tourist
destination (Yang et al., 2014)

8. ENTER 2017 Research Track Slide Number 8
Related work
• Artificial intelligence (AI) / data mining (DM) methods to
predict tourism demand
– Artificial neural networks (ANN) outperform decomposition models,
exponential smoothing, ARIMA and multiple regression (Kon & Turner 2005;
Song & Li 2008, p. 212; Law and Au 1999; Palmer et al. 2006; Lin et al. 2011)
– Rough set approach (decision rule induction) (Law and Au 1988; Goh et al. 2008)
– Genetic Algorithms for recognizing changes in composition of tourism
demand (Hernandez-López 2004; Hong et al. 2011)
– Support Vector Machines (SVM) superior to ARIMA models (Pai et al. 2006;
Chen & Wang 2007)

9. ENTER 2017 Research Track Slide Number 9
Content
• Introduction
• Related work
• Data collection and preparation
• Model building
• Results
• Conclusion and Outlook

10. ENTER 2017 Research Track Slide Number 10
Specification of data set
• Tourist arrival data for Swedish mountain destination Åre
– Total period: 77 months (December 2005 - April 2012)
– Sending countries: Denmark, Finland, Norway, Russia, United Kingdom
• Attributes
– Tourist arrivals: forecasting attribute
– Income: GDP per capita for each sending country
– Destination price level: CPI (Consumer Price Index) Sweden / CPI sending country
– Price level of alternative destinations
– Transportation costs: Jet fuel prices
– Advertising expenditures: Advertising investments by SkiStar Åre
– Web search traffic: Google Trends data (Åre, Åre winter, Åre ski, …)
– Mega event: dummy variable for FIS Alpine Ski World Championship

11. ENTER 2017 Research Track Slide Number 11
Specification of data set
Tourist arrivals and Google online traffic

12. ENTER 2017 Research Track Slide Number 12
Data preparation
• Time-lagged data
– Target attribute tourist arrivals is used with different time-lags as input
attributes (autoregressive approach)
– Time-lags are meaningful for other input attributes as well (e.g. web
search traffic, advertising expenditures)
– Data mining techniques (e.g. k-nearest neighbour) do not require
specific statistical characteristics to hold true for data set (e.g. non-
collinearity)
Input attributes can be included in data set for all different time-lags
within certain time window
– Time window of 24 months has been chosen to cope with seasonality
and trend over several years
Data set consists of
• Arrivals + big data attributes at time t-1 to t-24 = input attributes
• Tourist arrivals at time t = target attribute

13. ENTER 2017 Research Track Slide Number 13
Content
• Introduction
• Related work
• Data collection and preparation
• Model building
• Results
• Conclusion and Outlook

14. ENTER 2017 Research Track Slide Number 14
Model building
• Linear regression as statistical approach
– Forward selection (data-driven model building approach) to avoid
inclusion of irrelevant or highly correlated input attributes (caused by
including attributes with different time-lags)
• K-nearest neighbour as data mining approach
– Estimates target attribute of a new data entry, based on k most similar
data entries within training data set
– Two nearest neighbours (k=2) and Euclidian distance measure to
identify most similar past time windows
– Z-score standardisation to normalize attribute values
• Evaluation
– Prediction performance evaluated by sliding window validation
(moving a training and consecutive test window along data set)

15. ENTER 2017 Research Track Slide Number 15
Content
• Introduction
• Related work
• Data collection and preparation
• Model building
• Results
• Conclusion and Outlook

16. ENTER 2017 Research Track Slide Number 16
Results

17. ENTER 2017 Research Track Slide Number 17
Results
Autoregressive
approaches reach
good results in light
of an average of
3,223 arrivals and a
maximum of
14,199 arrivals per
month

18. ENTER 2017 Research Track Slide Number 18
Results
Adding big data
information
sources as
additional input
significantly
increases
prediction
performance
(supports research
proposition 1)

19. ENTER 2017 Research Track Slide Number 19
Results
MAE over all
sending countries
for the prediction
method k-NN is
reduced from 620
to 432, thus, by
30%

20. ENTER 2017 Research Track Slide Number 20
Results
Web search traffic
(i.e. Google trends),
price level of
alternative
destinations and
transportation costs
(i.e. jet fuel prices)
show strongest
influence on tourist
arrivals

21. ENTER 2017 Research Track Slide Number 21
Results
Predicting tourist arrivals by linear regression and big data

22. ENTER 2017 Research Track Slide Number 22
Results
Data mining
technique k-
nearest neighbour
(k-NN) outperforms
statistical approach
linear regression
(research
proposition 2)

23. ENTER 2017 Research Track Slide Number 23
Content
• Introduction
• Related work
• Data collection and preparation
• Model building
• Results
• Conclusion and Outlook

24. ENTER 2017 Research Track Slide Number 24
Conclusion and outlook
• Big data as input to tourist arrival prediction
– Big data information sources (e.g. web search traffic) show potential to
increase accuracy to predict tourist arrivals compared to
autoregressive approaches
– Advantage of enabling a prediction of tourist arrivals under changing
external conditions or singular events
• Data mining techniques instead of statistical approaches
– Data mining techniques (in this case k-NN) show potential to
outperform statistical approaches, like linear regression
– Can identify any kind of non-linear relationships; more robust against
biased data and violations of input attribute preconditions
• Outlook: Extend range of big data information sources
– Web navigation data, customer traffic data on social media platforms
(Twitter, Facebook, YouTube, etc.)

25. ENTER 2017 Research Track Slide Number 25
Wolfram Höpkena, Dominic Ernestia, Matthias Fuchsb,
Kai Kronenbergb, and Maria Lexhagenb
a Business Informatics Group
University of Applied Sciences Ravensburg-Weingarten, Germany
{name.surname}@hs-weingarten.de
b European Tourism Research Institute (ETOUR)
Mid-Sweden University, Sweden
{name.surname}@miun.se
Big data as input for
predicting tourist arrivals