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Foreign arrivals nowcasting in Italy with Google Trends data

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Abstract

The development of the ICT has deeply transformed the tourism industry. ICT has become a key determinant for competitiveness that deeply impacts on marketing and communication strategies. Online Travel Agency is accumulating a huge mass of valuable information. Web Data (Big Data) can actually represent an up-to-date information, which can be used as a support to improve statistical information, especially for monitoring current phenomena, as arrivals, spent nights, or the average length of stay. In this respect, an interesting issue is the assessment of the contribution of Web data for forecasting tourism flows. Specifically, nowcasting is a special case of forecasting as it deals with the knowledge of the present, immediate past and very near future. The aim of the paper is to assess the effective advantage of Google Trends (GT) data in forecasting tourist arrivals in Italy. The analysis is related to monthly foreign arrivals in tourist accommodations facilities. Google Trends data are used to predict the monthly number of foreign arrivals released by the Italian national statistical office, which is the dependent variable. Specifically, we have assessed the contribution of lagged GT variables in a standard ARIMA model and in a time series regression model with seasonal dummies and autoregressive components.

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Fig. 1

Source: ISTAT

Fig. 2

Source: Google Trends. March, 2017

Fig. 3

Source: ISTAT and Google Trends

Fig. 4
Fig. 5

Source: Our elaboration of ISTAT data

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Notes

  1. This is the acronym of the European Economic Activity Classification.

  2. See for example: https://statswiki.unece.org/display/bigdata/Classification+of+Types+of+Big+Data.

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Authors and Affiliations

  1. University of Teramo, Teramo, Italy

    F. Antolini

  2. University of Florence, Florence, Italy

    L. Grassini

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  1. F. Antolini
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  2. L. Grassini
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Correspondence to F. Antolini.

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Antolini, F., Grassini, L. Foreign arrivals nowcasting in Italy with Google Trends data. Qual Quant 53, 2385–2401 (2019). https://doi.org/10.1007/s11135-018-0748-z

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