Abstract
AbstractFuzzy modeling of time (or dynamic) series is the independent direction of scientific and applied research. This direction includes a battery of problems, the methodology of which is based on the theory of fuzzy sets, fuzzy logic and fuzzy models (or fuzzy inference systems). The initial procedure for fuzzy modeling of time series is fuzzification of historical data obtained by observing on the basis of “soft measurements” of the behavior of a dynamic system for a certain period of time. The paper proposes a new rule of fuzzification of historical data, which is tested on the indicators of the Dow Jones Industrial Average, established by the results of daily trading on the US stock exchange by the usual arithmetic averaging of component indicators. The fuzzification procedure proposed in the given paper is implemented through a fuzzy inference system, which ensures that the membership functions of the corresponding fuzzy subsets of the discrete universe are found, covering the entire set of indicators of the Dow Jones index for more than a year.KeywordsDow Jones indexFuzzy setFuzzy time seriesFuzzy inference
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