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Zero-Inflated Time Series Clustering Via Ensemble Thick-Pen Transform

Author

Listed:
  • Minji Kim

    (University of North Carolina at Chapel Hill)

  • Hee-Seok Oh

    (Seoul National University)

  • Yaeji Lim

    (Chung-Ang University)

Abstract

This study develops a new clustering method for high-dimensional zero-inflated time series data. The proposed method is based on thick-pen transform (TPT), in which the basic idea is to draw along the data with a pen of a given thickness. Since TPT is a multi-scale visualization technique, it provides some information on the temporal tendency of neighborhood values. We introduce a modified TPT, termed ‘ensemble TPT (e-TPT)’, to enhance the temporal resolution of zero-inflated time series data that is crucial for clustering them efficiently. Furthermore, this study defines a modified similarity measure for zero-inflated time series data considering e-TPT and proposes an efficient iterative clustering algorithm suitable for the proposed measure. Finally, the effectiveness of the proposed method is demonstrated by simulation experiments and two real datasets: step count data and newly confirmed COVID-19 case data.

Suggested Citation

  • Minji Kim & Hee-Seok Oh & Yaeji Lim, 2023. "Zero-Inflated Time Series Clustering Via Ensemble Thick-Pen Transform," Journal of Classification, Springer;The Classification Society, vol. 40(2), pages 407-431, July.
    • Handle: RePEc:spr:jclass:v:40:y:2023:i:2:d:10.1007_s00357-023-09437-z
    DOI: 10.1007/s00357-023-09437-z
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    References listed on IDEAS

    as
    1. Lim, Hwa Kyung & Li, Wai Keung & Yu, Philip L.H., 2014. "Zero-inflated Poisson regression mixture model," Computational Statistics & Data Analysis, Elsevier, vol. 71(C), pages 151-158.
    2. Fryzlewicz, Piotr & Oh, H. S., 2011. "Thick pen transformation for time series," LSE Research Online Documents on Economics 37663, London School of Economics and Political Science, LSE Library.
    3. 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.
    4. Fryzlewicz, Piotr & Ombao, Hernando, 2009. "Consistent Classification of Nonstationary Time Series Using Stochastic Wavelet Representations," Journal of the American Statistical Association, American Statistical Association, vol. 104(485), pages 299-312.
    5. P. Fryzlewicz & H.‐S. Oh, 2011. "Thick pen transformation for time series," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 73(4), pages 499-529, September.
    6. Lawrence Hubert & Phipps Arabie, 1985. "Comparing partitions," Journal of Classification, Springer;The Classification Society, vol. 2(1), pages 193-218, December.
    7. Leisch, Friedrich, 2006. "A toolbox for K-centroids cluster analysis," Computational Statistics & Data Analysis, Elsevier, vol. 51(2), pages 526-544, November.
    8. Fryzlewicz, Piotr & Ombao, Hernando, 2009. "Consistent classification of non-stationary time series using stochastic wavelet representations," LSE Research Online Documents on Economics 25162, London School of Economics and Political Science, LSE Library.
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