Skip to main content
Article thumbnail
Location of Repository

A methodology for qualitative learning in time series

By F. J. Cuberos, Juan A. Ortega and Luis González Francisco Velasco

Abstract

We present an off-line methodology for the identification of series. Given a learning set, an evaluation of the capacity of several alternatives to carry out correct identification is presented. For this, the series are transformed into symbol chains by means of several discretization methods. This transformation is done over typified and differenced series, translating the quantitative data to a qualitative description of the series evolution. Afterwards, a distance based on a kernel between literals is used to calculate the similarity between series, and a kneighbours algorithm is used to identify the class it belongs to. In the interval distance defined the similarity between symbols depends on the size and position of the intervals assigned to each symbol. The methodology has been tested with a television shares dataset presenting a high success identification ratio and it only need a neighbour to find the correct class. These characteristics are low influenced by size of the learning set

Year: 2008
OAI identifier: oai:CiteSeerX.psu:10.1.1.134.447
Provided by: CiteSeerX
Download PDF:
Sorry, we are unable to provide the full text but you may find it at the following location(s):
  • http://citeseerx.ist.psu.edu/v... (external link)
  • http://www.qrg.northwestern.ed... (external link)
  • Suggested articles


    To submit an update or takedown request for this paper, please submit an Update/Correction/Removal Request.