Using a Genetic Algorithm to Derive a Linguistic Summary of Trends in Numerical Time Series

The purpose of this paper is to propose a new easily implementable approach to a linguistic summarization of trends that may occur in temporal data, to be more specific - time series. To characterize the trends in time series, we use three parameters: dynamics of change, duration and variability, and apply to them the fuzzy linguistic summaries of data (databases) in the sense of Yager (cf. Yager (1982), Kacprzyk and Yager (2001) and Kacprzyk et al. (2000)) which in the form of natural language-like sentences subsume the very essence of a set of data. A genetic algorithm is used to generate the linguistic summaries sought