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

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초록

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.

키워드

Clustering; Multiscale method; Newly confirmed COVID-19 case data; Step count data; Thick-pen transform; Zero-inflated time series data
제목
Zero-Inflated Time Series Clustering Via Ensemble Thick-Pen Transform
저자
Kim, Minji; Oh, Hee-Seok; Lim, Yaeji
DOI
10.1007/s00357-023-09437-z
발행일
2023-07
유형
Article
저널명
Journal of Classification
권
40
호
2
페이지
407 ~ 431

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