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Dynamic Time Warping: Intertemporal Clustering Alignments for Hotel Tourism Demand

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  • Miguel Ángel Ruiz Reina

    (Universidad de Málaga)

Abstract

The consideration of the study on dynamic cluster flows in international tourists is an aspect that has been scarcely addressed in research despite its importance in economic development. Dynamic Time Warping is the methodology applied to identify alignments of common patterns in hotel demand time series within applied economics. The automatic determination of the number of clusters proposes an optimal number of groups for tourist destinations, and this proposition is confirmed through internal validation. Similarities among time series, including identifying outliers through boxplots, have been identified through the applied methodology. It has been employed for the primary tourist destinations in Spain for 106 international hotel demand time series. The effects of COVID-19 on the tourism sector and temporal similarities have been observed through clustering. The results that have been obtained reveal international tourist market flows that go beyond traditional analyses of seasonality or climatic factors, thus constituting a valuable tool for economic analysis in both direct and indirect markets.

Suggested Citation

  • Miguel Ángel Ruiz Reina, 2025. "Dynamic Time Warping: Intertemporal Clustering Alignments for Hotel Tourism Demand," Computational Economics, Springer;Society for Computational Economics, vol. 65(5), pages 2625-2648, May.
    • Handle: RePEc:kap:compec:v:65:y:2025:i:5:d:10.1007_s10614-024-10656-8
    DOI: 10.1007/s10614-024-10656-8
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    References listed on IDEAS

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    1. Boto-García, David & Mayor, Matías, 2022. "Domestic tourism and the resilience of hotel demand," Annals of Tourism Research, Elsevier, vol. 93(C).
    2. Wang, Gang-Jin & Xie, Chi & Han, Feng & Sun, Bo, 2012. "Similarity measure and topology evolution of foreign exchange markets using dynamic time warping method: Evidence from minimal spanning tree," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 391(16), pages 4136-4146.
    3. E. J. Michael, 2003. "Tourism Micro-Clusters," Tourism Economics, , vol. 9(2), pages 133-145, June.
    4. Wang, Xiaoyu & Sun, Yanlin & Peng, Bin, 2023. "Industrial linkage and clustered regional business cycles in China," International Review of Economics & Finance, Elsevier, vol. 85(C), pages 59-72.
    5. Theodore M. Crone, 2005. "An Alternative Definition of Economic Regions in the United States Based on Similarities in State Business Cycles," The Review of Economics and Statistics, MIT Press, vol. 87(4), pages 617-626, November.
    6. Giorgino, Toni, 2009. "Computing and Visualizing Dynamic Time Warping Alignments in R: The dtw Package," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 31(i07).
    7. Philip Hans Franses & Thomas Wiemann, 2020. "Intertemporal Similarity of Economic Time Series: An Application of Dynamic Time Warping," Computational Economics, Springer;Society for Computational Economics, vol. 56(1), pages 59-75, June.
    8. Bangzhu Zhu & Ping Wang & Julien Chevallier & Yiming Wei, 2015. "Carbon Price Analysis Using Empirical Mode Decomposition," Computational Economics, Springer;Society for Computational Economics, vol. 45(2), pages 195-206, February.
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    JEL classification:

    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
    • C50 - Mathematical and Quantitative Methods - - Econometric Modeling - - - General
    • C55 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Large Data Sets: Modeling and Analysis
    • C87 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Econometric Software
    • L83 - Industrial Organization - - Industry Studies: Services - - - Sports; Gambling; Restaurants; Recreation; Tourism

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