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.
Cerqueti, Roy; D'Urso, Pierpaolo; De Giovanni, Livia; Mattera, Raffaele; Vitale, Vincenzina. (2022). INGARCH-based fuzzy clustering of count time series with a football application. MACHINE LEARNING WITH APPLICATIONS, (ISSN: 2666-8270), 10: 1-11. Doi: 10.1016/j.mlwa.2022.100417.
INGARCH-based fuzzy clustering of count time series with a football application
De Giovanni, Livia;
2022
Abstract
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.
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