IDEAS home Printed from https://ideas.repec.org/a/kap/compec/v65y2025i5d10.1007_s10614-024-10656-8.html
   My bibliography  Save this article

Dynamic Time Warping: Intertemporal Clustering Alignments for Hotel Tourism Demand

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s10614-024-10656-8
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s10614-024-10656-8?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Boto-García, David & Mayor, Matías, 2022. "Domestic tourism and the resilience of hotel demand," Annals of Tourism Research, Elsevier, vol. 93(C).
    2. E. J. Michael, 2003. "Tourism Micro-Clusters," Tourism Economics, , vol. 9(2), pages 133-145, June.
    3. 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.
    4. 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.
    5. 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).
    6. 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.
    7. 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.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. repec:ipg:wpaper:2014-441 is not listed on IDEAS
    2. Miljkovic, Dragan & Vatsa, Puneet, 2023. "On the linkages between energy and agricultural commodity prices: A dynamic time warping analysis," International Review of Financial Analysis, Elsevier, vol. 90(C).
    3. repec:ipg:wpaper:2014-546 is not listed on IDEAS
    4. Amato, Umberto & Antoniadis, Anestis & De Feis, Italia & Goude, Yannig & Lagache, Audrey, 2021. "Forecasting high resolution electricity demand data with additive models including smooth and jagged components," International Journal of Forecasting, Elsevier, vol. 37(1), pages 171-185.
    5. Mastroeni, Loretta & Mazzoccoli, Alessandro & Quaresima, Greta & Vellucci, Pierluigi, 2021. "Decoupling and recoupling in the crude oil price benchmarks: An investigation of similarity patterns," Energy Economics, Elsevier, vol. 94(C).
    6. repec:ipg:wpaper:2014-422 is not listed on IDEAS
    7. repec:ipg:wpaper:2014-442 is not listed on IDEAS
    8. Christiane Baumeister & Danilo Leiva-León & Eric Sims, 2024. "Tracking Weekly State-Level Economic Conditions," The Review of Economics and Statistics, MIT Press, vol. 106(2), pages 483-504, March.
    9. repec:ipg:wpaper:2014-552 is not listed on IDEAS
    10. Christoph J. Borner & Ingo Hoffmann & Jonas Krettek & Lars M. Kurzinger & Tim Schmitz, 2021. "Bitcoin: Like a Satellite or Always Hardcore? A Core-Satellite Identification in the Cryptocurrency Market," Papers 2105.12336, arXiv.org.
    11. Kuhlmann, Angela & Decker, Christopher S. & Wohar, Mark E., 2008. "The Composition of Industry and the Duration of State Recessions," Journal of Regional Analysis and Policy, Mid-Continent Regional Science Association, vol. 38(3), pages 1-16.
    12. Jamal Bouoiyour & Refk Selmi & Aviral Kumar Tiwari & Olaolu Richard Olayeni, 2015. "What Determines Bitcoin’s Value?," Working papers of CATT hal-01880330, HAL.
    13. Quande Qin & Huangda He & Li Li & Ling-Yun He, 2020. "A Novel Decomposition-Ensemble Based Carbon Price Forecasting Model Integrated with Local Polynomial Prediction," Computational Economics, Springer;Society for Computational Economics, vol. 55(4), pages 1249-1273, April.
    14. Beckworth, David, 2010. "One nation under the fed? The asymmetric effects of US monetary policy and its implications for the United States as an optimal currency area," Journal of Macroeconomics, Elsevier, vol. 32(3), pages 732-746, September.
    15. Karpf, Andreas & Mandel, Antoine & Battiston, Stefano, 2018. "Price and network dynamics in the European carbon market," Journal of Economic Behavior & Organization, Elsevier, vol. 153(C), pages 103-122.
    16. repec:ipg:wpaper:2014-542 is not listed on IDEAS
    17. Vatsa, Puneet & Miljkovic, Tatjana & Miljkovic, Dragan, 2024. "Price discovery redux—Analyzing energy spot and futures prices using a dynamic programming approach," Energy Economics, Elsevier, vol. 140(C).
    18. Hanjo Odendaal & Monique Reid & Johann F. Kirsten, 2020. "Media‐Based Sentiment Indices as an Alternative Measure of Consumer Confidence," South African Journal of Economics, Economic Society of South Africa, vol. 88(4), pages 409-434, December.
    19. Yangchen Di & Mingyue Lu & Min Chen & Zhangjian Chen & Zaiyang Ma & Manzhu Yu, 2022. "A quantitative method for the similarity assessment of typhoon tracks," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 112(1), pages 587-602, May.
    20. Wong, Kin Ming & Chong, Terence Tai Leung, 2014. "A Tale of Two Regimes: Classifying and Revisiting the Monetary Policy Regimes," MPRA Paper 75922, University Library of Munich, Germany.
    21. Saldivia, Mauricio & Kristjanpoller, Werner & Olson, Josephine E., 2020. "Energy consumption and GDP revisited: A new panel data approach with wavelet decomposition," Applied Energy, Elsevier, vol. 272(C).
    22. Bangzhu Zhu & Shujiao Ma & Rui Xie & Julien Chevallier & Yi-Ming Wei, 2018. "Hilbert Spectra and Empirical Mode Decomposition: A Multiscale Event Analysis Method to Detect the Impact of Economic Crises on the European Carbon Market," Computational Economics, Springer;Society for Computational Economics, vol. 52(1), pages 105-121, June.
    23. Corey Ducharme & Bruno Agard & Martin Trépanier, 2024. "Improving demand forecasting for customers with missing downstream data in intermittent demand supply chains with supervised multivariate clustering," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 43(5), pages 1661-1681, August.
    24. repec:ipg:wpaper:2014-494 is not listed on IDEAS
    25. Sokhna Dieng & Pierre Michel & Abdoulaye Guindo & Kankoe Sallah & El-Hadj Ba & Badara Cissé & Maria Patrizia Carrieri & Cheikh Sokhna & Paul Milligan & Jean Gaudart, 2020. "Application of Functional Data Analysis to Identify Patterns of Malaria Incidence, to Guide Targeted Control Strategies," IJERPH, MDPI, vol. 17(11), pages 1-23, June.
    26. repec:ipg:wpaper:2014-569 is not listed on IDEAS
    27. Kristie M. Engemann & Michael T. Owyang & Howard J. Wall, 2014. "Where Is An Oil Shock?," Journal of Regional Science, Wiley Blackwell, vol. 54(2), pages 169-185, March.
    28. repec:ipg:wpaper:2014-469 is not listed on IDEAS
    29. Liu, Shuihan & Xie, Gang & Wang, Zhengzhong & Wang, Shouyang, 2024. "A secondary decomposition-ensemble framework for interval carbon price forecasting," Applied Energy, Elsevier, vol. 359(C).

    More about this item

    Keywords

    Dynamic time warping; Unsupervised clustering; Hotel tourism demand;
    All these keywords.

    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

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:kap:compec:v:65:y:2025:i:5:d:10.1007_s10614-024-10656-8. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.