The article considers the problem of forecasting prices on the commodity market for the year ahead, broken down by months. The uncertainty level of the forecast increases with the increase of its horizon, due to changes caused by events of the external environment on the forecasting horizon. To reduce this uncertainty, we have proposed a hybrid model for the formation and correction of a monthly price forecast for the year ahead. This model uses for forecasting expert-analytical information processed using the FCM model, and data from time series of commodity market prices and macro indicators. Model of forecasting has based on the use of an ensemble of VECM and VAR models built at various time scales. For correcting the target indicator forecast on the forecasting horizon we have developed an algorithm based on signals of situation monitoring conducted on the FСМ-model of commodity market situation. We demonstrate the efficiency of the proposed technique for monthly forecasting of prices for black scrap a year ahead for 2019. The accuracy of the obtained forecast we compared with naive and ARIMA forecasts.