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        <title type="main" level="a">Short-term forecasts on time series for tourism in Lombardy</title>
        <author>
          <persName n="1" ref="https://orcid.org/0000-0002-4050-5316" type="ORCID">
            <forename>Andrea</forename>
            <surname>Marletta</surname>
            <placeName type="affiliation">University of Milano-Bicocca, Italy</placeName>
          </persName>
          <persName n="2" ref="https://orcid.org/0000-0003-4586-9044" type="ORCID">
            <forename>Roberta</forename>
            <surname>Rossi</surname>
            <placeName type="affiliation">PoliS-Lombardia, Italy</placeName>
          </persName>
          <persName n="3">
            <forename>Elena</forename>
            <surname>Diceglie</surname>
            <placeName type="affiliation">PoliS-Lombardia, Italy</placeName>
          </persName>
        </author>
        <respStmt>
          <resp>This is a section of <title>ASA 2022 Data-Driven Decision Making</title>(DOI: <idno type="DOI">10.36253/979-12-215-0106-3</idno>) by </resp>
          <name>Enrico di Bella, Luigi Fabbris, Corrado Lagazio</name>
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      </titleStmt>
      <publicationStmt>
        <publisher>Firenze University Press</publisher>
        <pubPlace>Firenze</pubPlace>
        <date when="2023">2023</date>
        <idno type="DOI">https://doi.org/10.36253/979-12-215-0106-3.14</idno>
        <availability>
          <p>Available for academic research purposes</p>
          <p>Open Access</p>
          <p>Copyright Author(s)</p>
          <licence source="text" target="https://creativecommons.org/licenses/by/4.0/legalcode">
            <p>Content licence CC BY 4.0</p>
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            <p>Metadata licence CC0 1.0</p>
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        <p>This is original content, published for academic research purposes</p>
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      <abstract xml:lang="en">
        <p>Data from official sources on nights spent in an accommodation for tourists in Lombardy are available until 2021. These data on touristic flows for 2020 and 2021 registered a clear downfall because of restrictions related to Covid-19. The aim of this paper is to verify the presence of a full or partial recover of tourists in provinces of Lombardy using short-term predictions for 2022. A time-series procedure has been applied to obtain a forecast estimate for 2022 using an ARMA model with the addition of an exogenous variable. The hypothesis at the basis of the model is that a punctual estimate of the touristic flows could be obtained using an auxiliary variable explaining the number of employees in the food services and hospitality industry. This auxiliary variable is represented as the difference between the number of starting work contracts and the contract terminations. These data are available thanks to the Informative system of mandatory communications provided by the Italian Minister of Labour. The availability of this information is daily guaranteed at level of single municipality but for the purpose of this paper, data have been aggregated at province level. The short-term predictions obtained for 2022 have been used to verify the presence of a recovery respect to the pandemic emergency of Covid-19 using a double growth rate. A first growth rate has been computed comparing the number of estimated tourists respect to the 2021 measuring the existence of a rebound after the restrictions. A second growth rate measured the estimates for 2021 respect to the presences of 2019 to monitor the trends in Lombardy compared to the before Covid-19 period. Preliminary results showed an evident upswing respect to 2021 and a partial recovery respect to 2019 for the majority of Lombard provinces.</p>
      </abstract>
      <textClass>
        <keywords>
          <list>
            <item>Time series</item>
            <item>Forecasts</item>
            <item>Tourism</item>
          </list>
        </keywords>
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      <p>It is available online at https://doi.org/10.36253/979-12-215-0106-3.14<ref target="https://doi.org/10.36253/979-12-215-0106-3.14" /></p>
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          <head>References</head>
          <bibl n="111834">Box, G. E., Jenkins, G. M., Reinsel, G. C., Ljung, G. M. (2015). Time series analysis: forecast-ing and control. John Wiley &amp;amp; Sons.</bibl>
          <bibl n="111835">Fotiadis, A., Polyzos, S., Huan, T. C. T. (2021). The good, the bad and the ugly on COVID-19 tourism recovery. Annals of tourism research, 87, 103-117.</bibl>
          <bibl n="111836">Hamilton, J. D. (2020). Time series analysis. Princeton university press.</bibl>
          <bibl n="111837">Hyndman, R. J., Athanasopoulos, G. (2018). Forecasting: principles and practice. OTexts.</bibl>
          <bibl n="111838">Provenzano, D., Volo, S. (2022). Tourism recovery amid COVID-19: The case of Lombardy, Italy. Tourism Economics, 28(1), 110-130.</bibl>
          <bibl n="111839">Wei, W. W. (2006). Time series analysis: univariate and multivariate. Methods. Boston, MA: Pearson Addison Wesley.</bibl>
          <bibl n="111840">Yeh, S. S. (2021). Tourism recovery strategy against COVID-19 pandemic. Tourism Recreation Research, 46(2), 188-194.</bibl>
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