In this study the problem of short-term forecasting for coal and crude oil production in Ukraine within the period of 2008–2012 is considered. Linear autoregressive and autoregressive moving average models as well as optimal filtering algorithm for linear systems (Kalman filter) based upon autoregressive model of second order were constructed for short term forecasting. The Kalman filter was successfully applied for generating optimal estimates of states and short-term forecasts. The state noise covariances were estimated recursively with new data coming what corresponds to the general ideology of adaptation. The best forecasting results for coal and crude oil production were received for autoregressive models with optimal filter and for ARMA models

Autoregressive model, autoregressive moving average model, Kalman filter, coal and crude oil production, short-term forecasting