Linguistic summaries of time series via an OWA operator based aggregation of partial trends

We extend our approach to the linguistic summarization of (numerical) time series. The main issue boils down to the identification of trends in time series that are characterized by a set of attributes followed by their appropriate aggregation. We propose to use the OWA (ordered weighted averaging) operators for the aggregation of partial trends as an alternative to the use of the classic Zadeh's calculus of linguistically quantified propositions, the Sugeno integral and the Choquet integral. The use of the OWA operators provides a convenient unified aggregation means that can be used to derive diverse types of summaries. The results obtained confirm a high human consistency of linguistic summaries derived.