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Advances in Data Analysis and Classification

Erschienen in:

20.05.2020 | Regular Article

Clustering discrete-valued time series

verfasst von: Tyler Roick, Dimitris Karlis, Paul D. McNicholas

Erschienen in: Advances in Data Analysis and Classification | Ausgabe 1/2021

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Abstract

There is a need for the development of models that are able to account for discreteness in data, along with its time series properties and correlation. Our focus falls on INteger-valued AutoRegressive (INAR) type models. The INAR type models can be used in conjunction with existing model-based clustering techniques to cluster discrete-valued time series data. With the use of a finite mixture model, several existing techniques such as the selection of the number of clusters, estimation using expectation-maximization and model selection are applicable. The proposed model is then demonstrated on real data to illustrate its clustering applications.

Vorheriger Artikel Kappa coefficients for dichotomous-nominal classifications

Nächster Artikel Simultaneous dimension reduction and clustering via the NMF-EM algorithm

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Titel: Clustering discrete-valued time series
verfasst von: Tyler Roick
Dimitris Karlis
Paul D. McNicholas
Publikationsdatum: 20.05.2020
Verlag: Springer Berlin Heidelberg
Erschienen in: Advances in Data Analysis and Classification / Ausgabe 1/2021
Print ISSN: 1862-5347
Elektronische ISSN: 1862-5355
DOI: https://doi.org/10.1007/s11634-020-00395-7

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