Clustering of interval time series

• Tipo: Artigo em Revista Científica Internacional
• Data: 2019

Autores

Maharaj, EA (Autor) Outra; Paulo Teles (Autor) FEP; brito, p (Autor) FEP

Revista

Título: Statistics and Computing

Vol. 29

Páginas: 1011-1034

ISSN: 0960-3174

Editora: Springer Nature

Indexação

ISI Web of Knowledge - 11 Citações

Scopus - 13 Citações

Outras Informações

ID Authenticus: P-00Q-5RW

DOI: 10.1007/s11222-018-09851-z

Abstract (EN): Interval time series occur when real intervals of some variable of interest are registered as an ordered sequence along time. We address the problem of clustering interval time series (ITS), for which different approaches are proposed. First, clustering is performed based on point-to-point comparisons. Time-domain and wavelet features also serve as clustering variables in alternative approaches. Furthermore, autocorrelation matrix functions, gathering the autocorrelation and cross-correlation functions of the ITS upper and lower bounds, may be compared using adequate distances (e.g. the Frobenius distance) and used for clustering ITS. An improved procedure to determine the autocorrelation function of ITS is proposed, which also serves as a basis for clustering. The different alternative approaches are explored and their performances compared for ITS simulated under different setups. An application to sea level daily ranges, observed at different locations in Australia, illustrates the proposed methods.

Idioma: Inglês

Tipo (Avaliação Docente): Científica

Nº de páginas: 24