Clustering of interval time series

• Type: Article in International Scientific Journal
• Date: 2019

Authors

Maharaj, EA (Author) Other; Paulo Teles (Author) FEP; brito, p (Author) FEP

Journal

Title: Statistics and Computing

Vol. 29

Pages: 1011-1034

ISSN: 0960-3174

Publisher: Springer Nature

Indexing

ISI Web of Knowledge - 11 Citations

Scopus - 13 Citations

Other information

Authenticus ID: 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.

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 24