INGARCH-based fuzzy clustering of count time series with a football application
Roy Cerqueti, Pierpaolo D’Urso, Livia De Giovanni, Raffaele Mattera, Vincenzina Vitale · Machine Learning with Applications · 2022
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.