Time dependent clustering of time series

• Type: Article in International Conference Proceedings Book
• Date: 2007

Authors

Joaquim F. Pinto da Costa (Author) FCUP; Isabel Silva (Author) FEUP; M. Eduarda Silva (Author) FEP

Conference proceedings International

Title: Proceedings of the 56th Session of the ISI

Pages: 1-4

56th Session of the ISI - International Statistical Institute

Lisboa, Portugal, 22 a 29 de Agosto de 2007

Scientific classification

FOS: Natural sciences > Mathematics

CORDIS: Physical sciences > Mathematics > Statistics

Other information

Abstract (EN): In this work we consider the problem of clustering time series. Contrary to other works on this topic, our main concern is to let the most important observations, for instance the most recent, have a larger weight on the analysis. This is done by defining similarities measures between two time series, based on Pearson's correlation coefficient, which uses the notion of weighted mean and weighted covariance, where the weights increase monotonically with the time. We use these measures, which are metrics between time series, as a similarity or dissimilarity index between the $n$ time series to be clustered. We apply a very well known partitional method, the K-means, with some adaptations to make it able to choose the number of clusters.

Language: English

Type (Professor's evaluation): Scientific

Notes: http://isi.cbs.nl/iamamember/CD7-Lisboa2007/Bulletin-of-the-ISI-Volume-LXII-2007.pdf

No. of pages: 4