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We present a new approach to the evaluation of the similarity of time series. We consider, as in our previous works, the case of linguistic summarization of the past performance of an investment (mutual) fund and its underlying benchmark which is very important for an effective and efficient investment decision support. We propose a new comparison method based not on the comparison of the consecutive...
We further extend our approach to the linguistic summarization of time series (cf. Kacprzyk, Wilbik and Zadrozny) in which an approach based on a calculus of linguistically quantified propositions is employed, and the essence of the problem is equated with a linguistic quantifier driven aggregation of partial scores (trends). In addition to the basic criterion of a degree of truth (validity), we also...
We present an approach to a more efficient generation of linguistic summaries using the traditional degree of truth and a degree of focus based mechanism for discarding nonpromising summaries. We use the approach to derive linguistic data summaries to subsume the past performance of an investment (mutual) fund, and then present numerical results on the efficiency of the truncation process proposed...
Taking as a point of departure our works on linguistic summarization of time series (cf. Kacprzyk, Wilbik and Zadrozny [1], [2], [3], [4], [5], [6], [7], [8], [9], [10], [11]) in which an approach based on a calculus of linguistically quantified propositions was proposed, and the essence of the problem was equated with a linguistic quantifier driven aggregation of partial scores (trends), we present...
We propose here some new types of linguistic summaries of time series by extending our previous works. First, the linguistic summaries of time series refer to the summaries of (partial) trends identified here with straight line segments of a piece-wise linear approximation of a time series that is proposed in the paper. To characterize the trends we use the slope of the line segment, the goodness...
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...
Linguistic summaries as descriptions of trends in time series data are proposed. Two general types of such summaries are discussed. The point of departure are linguistic summaries of databases due to Yager. The specificity of time series summarization requires a more general approach to linguistic quantifier based aggregation. Sugeno integrals are employed to address this problem.
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