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

被引：5
|
作者：
Cerqueti, Roy [1 ,2 ,3 ]
D'Urso, Pierpaolo [1 ]
De Giovanni, Livia [4 ]
Mattera, Raffaele [1 ]
Vitale, Vincenzina [1 ]
机构：
[1] Sapienza Univ Rome, Dept Social & Econ Sci, Rome, Italy
[2] London South Bank Univ, Sch Business, London, England
[3] Univ Angers, GRANEM, Angers, France
[4] LUISS Guido Carli, Dept Polit Sci, Rome, Italy
来源：
MACHINE LEARNING WITH APPLICATIONS | 2022年 / 10卷
关键词：
Fuzzy C-medoids; INGARCH; Poisson distribution; Sport analytics; BIVARIATE POISSON MODEL;
D O I：
10.1016/j.mlwa.2022.100417
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
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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页数：11
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