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.

引用

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页数：11

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