Doctor of Economical Sciences, Professor of the Department of mathematical methods and models in Economics, Orenburg State University

Objective: to develop mathematical models for predicting fuel prices. Research methods: in the course of the study, adaptive methods for forecasting one-dimensional time series, methods of fuzzy time series were applied. The relevance of the study: the price of gasoline and diesel fuel are one of the determining factors for the economy of the region as a whole, since they indirectly affect all pricing. Rising prices for diesel fuel, widely used in agriculture, ultimately leads to higher prices for food. The cost of fuel and fuel and lubricants - the main item of expenditure of transport companies and directly affects the tariffs of carriers. Thus, the development of forecasts for regional fuel prices is relevant. Results: The developed models for predicting fuel prices provide an idea of the price level for main types of fuels in the short term, which makes it possible for economic entities to make effective management decisions based on planning fuel costs.

Keywords: adaptive models, fuel price forecast, fuzzy time series.

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