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Classification Of Short Time Series

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Author Info
Agostino Tarsitano () (Dipartimento di Economia e Statistica, Università della Calabria)
Abstract

Many time series are of short duration because data acquisition has, of necessity, proceeded for but a brief term. Such data have previously often been analyzed by methods that either do not explicitly take into account time related changes or that are designed for long time series. In this paper, we consider several ways of assigning a dissimilarity between univariate time series in short term behavior. In particular, we have defined a measure that works irrespective of different baselines and scaling factors and its effectiveness has been evaluated on real and synthetic data sets

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File URL: http://www.ecostat.unical.it/RePEc/WorkingPapers/WP05_2009.pdf
File Format: application/pdf
File Function: First version, 2009-02
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Publisher Info
Paper provided by Università della Calabria, Dipartimento di Economia e Statistica in its series Working Papers with number 200905.

Download reference. The following formats are available: HTML (with abstract), plain text (with abstract), BibTeX, RIS (EndNote, RefMan, ProCite), ReDIF
Length: 14 pages
Date of creation: Feb 2009
Date of revision:
Handle: RePEc:clb:wpaper:200905

Contact details of provider:
Postal: Università della Calabria, Dipartimento di Economia e Statistica, Ponte Pietro Bucci, Cubo 0/C, I-87036 Arcavacata di Rende, CS, Italy
Phone: +39 0984 492413
Fax: +39 0984 492421
Web page: http://www.ecostat.unical.it/
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Related research
Keywords: Time trajectories; Distances; PAM clustering; Representative trends;

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This page was last updated on 2009-12-2.


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