Entropy-based fuzzy clustering of interval-valued time series

被引：0

作者：

Vitale, Vincenzina ^{[1
]}

D'Urso, Pierpaolo ^{[1
]}

De Giovanni, Livia ^{[2
]}

Mattera, Raffaele ^{[1
]}

机构：

[1] Sapienza Univ Rome, Dept Social Sci & Econ, Piazzale Aldo Moro 5, I-00185 Rome, Italy

[2] Luiss Univ, Dept Polit Sci & Data Lab, Viale Romania 32, I-00197 Rome, Italy

来源：

ADVANCES IN DATA ANALYSIS AND CLASSIFICATION | 2024年

关键词：

Interval-valued time series; Fuzzy clustering; Dynamic time warping; FTSE-MIB index; ALGORITHMS; EXTENSION;

D O I：

10.1007/s11634-024-00586-6

中图分类号：

O21 [概率论与数理统计]; C8 [统计学];

学科分类号：

020208 ; 070103 ; 0714 ;

摘要：

This paper proposes a fuzzy C-medoids-based clustering method with entropy regularization to solve the issue of grouping complex data as interval-valued time series. The dual nature of the data, that are both time-varying and interval-valued, needs to be considered and embedded into clustering techniques. In this work, a new dissimilarity measure, based on Dynamic Time Warping, is proposed. The performance of the new clustering procedure is evaluated through a simulation study and an application to financial time series.

引用

页数：27

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