Zero-Inflated Time Series Clustering Via Ensemble Thick-Pen Transformopen access
- Authors
- Kim, Minji; Oh, Hee-Seok; Lim, Yaeji
- Issue Date
- Jul-2023
- Publisher
- SPRINGER
- Keywords
- Clustering; Multiscale method; Newly confirmed COVID-19 case data; Step count data; Thick-pen transform; Zero-inflated time series data
- Citation
- JOURNAL OF CLASSIFICATION, v.40, no.2, pp 407 - 431
- Pages
- 25
- Journal Title
- JOURNAL OF CLASSIFICATION
- Volume
- 40
- Number
- 2
- Start Page
- 407
- End Page
- 431
- URI
- https://scholarworks.bwise.kr/cau/handle/2019.sw.cau/67292
- DOI
- 10.1007/s00357-023-09437-z
- ISSN
- 0176-4268
1432-1343
- 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.
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Collections - College of Business & Economics > Department of Applied Statistics > 1. Journal Articles
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