A linguistic approach to a human-consistent summarization of time series using a SOM learned with a LVQ-Type algorithm

Authors:
Janusz Kacprzyk;Anna Wilbik;Sławomir Zadrożny
Affiliations:
Systems Research Institute, Polish Academy of Sciences, Warsaw, Poland;Systems Research Institute, Polish Academy of Sciences, Warsaw, Poland;Systems Research Institute, Polish Academy of Sciences, Warsaw, Poland
Venue:
ICANN'06 Proceedings of the 16th international conference on Artificial Neural Networks - Volume Part II
Year:
2006

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Abstract

The purpose of this paper is to propose a new, human consistent way to capture the very essence of a dynamic behavior of some sequences of numerical data. Instead of using traditional, notably statistical type analyses, we propose the use of fuzzy logic based linguistic summaries of data(bases) in the sense of Yager, later developed by Kacprzyk and Yager, and Kacprzyk, Yager and Zadrożny. Our main interest is in the summarization of trends characterized by: dynamics of change, duration and variability. To define the dynamic of change of the time series we propose to use for a preprocessing of data a SOM (self-organizing maps) learned with a LVQ (Learning Vector Quantization) algorithm, and then our approach for linguistic summaries of trends.