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Unraveling Meteorological Dynamics: A Two-Level Clustering Algorithm for Time Series Pattern Recognition with Missing Data Handling

Author

Listed:
  • Ekaterini Skamnia

    (Department of Civil Engineering, University of Patras, 265 04 Patras, Greece
    These authors contributed equally to this work.)

  • Eleni S. Bekri

    (Department of Civil Engineering, University of Patras, 265 04 Patras, Greece
    These authors contributed equally to this work.)

  • Polychronis Economou

    (Department of Civil Engineering, University of Patras, 265 04 Patras, Greece)

Abstract

Identifying regions with similar meteorological features is of both socioeconomic and ecological importance. Towards that direction, useful information can be drawn from meteorological stations, and spread in a broader area. In this work, a time series clustering procedure composed of two levels is proposed, focusing on clustering spatial units (meteorological stations) based on their temporal patterns, rather than clustering time periods. It is capable of handling univariate or multivariate time series, with missing data or different lengths but with a common seasonal time period. The first level involves the clustering of the dominant features of the time series (e.g., similar seasonal patterns) by employing K-means, while the second one produces clusters based on secondary features. Hierarchical clustering with Dynamic Time Warping for the univariate case and multivariate Dynamic Time Warping for the multivariate scenario are employed for the second level. Principal component analysis or Classic Multidimensional Scaling is applied before the first level, while an imputation technique is applied to the raw data in the second level to address missing values in the dataset. This step is particularly important given that missing data is a frequent issue in measurements obtained from meteorological stations. The method is subsequently applied to the available precipitation time series and then also to a time series of mean temperature obtained by the automated weather stations network in Greece. Further, both of the characteristics are employed to cover the multivariate scenario.

Suggested Citation

  • Ekaterini Skamnia & Eleni S. Bekri & Polychronis Economou, 2025. "Unraveling Meteorological Dynamics: A Two-Level Clustering Algorithm for Time Series Pattern Recognition with Missing Data Handling," Stats, MDPI, vol. 8(2), pages 1-39, May.
    • Handle: RePEc:gam:jstats:v:8:y:2025:i:2:p:36-:d:1652391
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    References listed on IDEAS

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    1. Harvey, Andrew C. & Trimbur, Thomas M. & Van Dijk, Herman K., 2007. "Trends and cycles in economic time series: A Bayesian approach," Journal of Econometrics, Elsevier, vol. 140(2), pages 618-649, October.
    2. Godsill, Simon J. & Doucet, Arnaud & West, Mike, 2004. "Monte Carlo Smoothing for Nonlinear Time Series," Journal of the American Statistical Association, American Statistical Association, vol. 99, pages 156-168, January.
    3. J. Kruskal, 1964. "Multidimensional scaling by optimizing goodness of fit to a nonmetric hypothesis," Psychometrika, Springer;The Psychometric Society, vol. 29(1), pages 1-27, March.
    4. Robert Tibshirani & Guenther Walther & Trevor Hastie, 2001. "Estimating the number of clusters in a data set via the gap statistic," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 63(2), pages 411-423.
    5. Kyriakos Skarlatos & Eleni S. Bekri & Dimitrios Georgakellos & Polychronis Economou & Sotirios Bersimis, 2023. "Projecting Annual Rainfall Timeseries Using Machine Learning Techniques," Energies, MDPI, vol. 16(3), pages 1-20, February.
    6. Yanhui Liu & Pierre Cizeau & Martin Meyer & Chung-Kang Peng & H. Eugene Stanley, 1997. "Correlations in Economic Time Series," Papers cond-mat/9706021, arXiv.org.
    7. Bekri, Eleni S. & Kokkoris, Ioannis P. & Skuras, Dimitrios & Hein, Lars & Dimopoulos, Panayotis, 2024. "Ecosystem accounting for water resources at the catchment scale, a case study for the Peloponnisos, Greece," Ecosystem Services, Elsevier, vol. 65(C).
    8. Mohammad Samsul Alam & Sangita Paul, 2020. "A comparative analysis of clustering algorithms to identify the homogeneous rainfall gauge stations of Bangladesh," Journal of Applied Statistics, Taylor & Francis Journals, vol. 47(8), pages 1460-1481, June.
    9. Liu, Yanhui & Cizeau, Pierre & Meyer, Martin & Peng, C.-K. & Eugene Stanley, H., 1997. "Correlations in economic time series," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 245(3), pages 437-440.
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