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Automatic Clustering for Seasonal Time Series Based on Entropy

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Theory and Applications of Time Series Analysis (ITISE 2022)

Part of the book series: Contributions to Statistics ((CONTRIB.STAT.))

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Abstract

Automatic clustering for seasonal time series based on entropy is a tool developed to understand decision-making behaviours for economic agents. An unsupervised learning system reduces information and is a powerful statistical learning tool. This method is a multiple-choice classification solution under uncertain environments. The empirical application is in the tourist accommodation market, where international tourists must choose various accommodation options (hotels, tourist apartments, campsites and rural apartments). Seasonal uncertainty for offers can solve information gaps in understanding human behaviour. The three-dimensional information of spatial extension, spatial location and temporal extension is offered for the Spanish tourist market of foreigners who visit the Spanish Autonomous Communities from January 2001 to June 2022. The results have revealed similarities and dissimilarities among the analysed Spanish regions depending on the seasonal period. In addition, the internal verification criteria have allowed us to quantify similarities in intragroup behaviour as an added value to this study.

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Authors and Affiliations

  1. Department of Applied Economics (Statistics and Econometrics), University of Malaga, Malaga, Spain

    Miguel Ángel Ruiz Reina & Miguel Ángel Ruiz Reina

Authors
  1. Miguel Ángel Ruiz Reina
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Correspondence to Miguel Ángel Ruiz Reina .

Editor information

Editors and Affiliations

  1. Faculty of Sciences, University of Granada, Granada, Spain

    Olga Valenzuela

  2. ETSIIT, CITIC-UGR, University of Granada, Granada, Spain

    Fernando Rojas

  3. ETSIIT, CITIC-UGR, University of Granada, Granada, Spain

    Luis Javier Herrera

  4. ETSIIT, CITIC-UGR, University of Granada, Granada, Spain

    Héctor Pomares

  5. ETSIIT, CITIC-UGR, University of Granada, Granada, Spain

    Ignacio Rojas

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Reina, M.Á.R. (2023). Automatic Clustering for Seasonal Time Series Based on Entropy. In: Valenzuela, O., Rojas, F., Herrera, L.J., Pomares, H., Rojas, I. (eds) Theory and Applications of Time Series Analysis. ITISE 2022. Contributions to Statistics. Springer, Cham. https://doi.org/10.1007/978-3-031-40209-8_7

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