INGARCH-based fuzzy clustering of count time series with a football application

Although there are many contributions in the time series clustering literature, few studies still deal with count time series data. This paper aims to develop a fuzzy clustering procedure for count time series data. We propose an Integer GARCH-based Fuzzy C-medoids (INGARCH-FCMd) method for clusteri...

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Bibliographic Details
Published in:Machine learning with applications 2022-12, Vol.10, p.100417, Article 100417
Main Authors: Cerqueti, Roy, D’Urso, Pierpaolo, De Giovanni, Livia, Mattera, Raffaele, Vitale, Vincenzina
Format: Article
Language:English
Subjects:
Economics and Finance
Fuzzy C-medoids
Humanities and Social Sciences
INGARCH
Poisson distribution
Sport analytics
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Summary:Although there are many contributions in the time series clustering literature, few studies still deal with count time series data. This paper aims to develop a fuzzy clustering procedure for count time series data. We propose an Integer GARCH-based Fuzzy C-medoids (INGARCH-FCMd) method for clustering count time series based on a Mahalanobis distance between the parameters estimated by an INGARCH model. We show how the proposed clustering method works by clustering football teams according to the number of scored goals.
ISSN:2666-8270
2666-8270
DOI:10.1016/j.mlwa.2022.100417