Linguistic Summarization of Time Series Under Different Granulation of Describing Features

We consider an extension to a new approach to the linguistic summarization of time series data proposed in our previous papers. We summarize trends identified here with straight segments of a piecewise linear approximation of time series. Then we employ, as a set of features, the duration, dynamics of change and variability, and assume different, human consistent granulations of their values. The problem boils down to a linguistic quantifier driven aggregation of partial trends that is done via the classic zadeh’s calculus of linguistically quantified propositions but with different t-norms. We show an application to linguistic summarization of time series data on daily quotations of an investment fund over an eight year period.keywordstime series datainvestment fundordered weight averagemining time series datadrive aggregationthese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.