V.S. Luferov – Post-graduate Student, National Research University «Moscow Power Engineering Institute»

E-mail: lyferov@yandex.ru

The formulation and solution of the problem of forecasting multidimensional time series based on fuzzy cognitive models

(FCM), allowing to take into account both direct and indirect influence of indicators on each other is considered. A new type of FCM is proposed that provides, in conditions of uncertainty, firstly the parametric adjustment of the models of aggregation of the effects of indicators and the weights of the influence of these models, secondly, the adjustment of fuzzy influence relations between FCM concepts; third, the structural adjustment of the model due to a change and the possibility of establishing different «depth of retrospect» for each input and output fuzzy indicators of FCM – one-dimensional time series.

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