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A methodology for qualitative learning in time series

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


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
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