The paper discusses a new predictive model of a fuzzy volatile time series, in the framework of which a new approach to the data fuzzification is proposed as the results of observations based on “Sоft Measurements”. As an example, the index of the Dow Jones Industrial Average is chosen, the readings of which are established by usual arithmetic averaging of cоntextual indicators. This allows to consider the daily readings of the Dow Jones index as weakly structured, and to interpret the dynamics of its change as a fuzzy time series. The data fuzzification is realized by applying the fuzzy inference system that provides the values of the membership functions of the appropriate fuzzy sets on the universe covering the set of Dow Jones index for the period from June 15, 2018 to October 10, 2019. The prоpоsed predictive mоdel is based on the identified internal relatiоnships, designed as 1^{st} оrder fuzzy relatiоns between evaluation criteria (or fuzzy sets) that describe weakly structured Dow Jones indexes. At the end of the study, the proposed model is evaluated for adequacy using the statistical criteria MAPE, MPE and MSE (pp.14-25).

**Keywords:**

*Dow Jones industrial average, Fuzzy time series, Fuzzy set, Fuzzy inference, Neural network, Aproximation*

**DOI**: 10.25045/jpis.v14.i1.02

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