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

Minji Kim, Hee-Seok Oh and Yaeji Lim ()
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Minji Kim: University of North Carolina at Chapel Hill
Hee-Seok Oh: Seoul National University
Yaeji Lim: Chung-Ang University

Journal of Classification, 2023, vol. 40, issue 2, No 8, 407-431

Abstract: 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.

Keywords: Clustering; Multiscale method; Newly confirmed COVID-19 case data; Step count data; Thick-pen transform; Zero-inflated time series data (search for similar items in EconPapers)
Date: 2023
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DOI: 10.1007/s00357-023-09437-z

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