Forest planning is always influenced by uncertain factors. Variations in growth, outcome of regeneration, timber prices, costs and mortality cannot be avoided, whereas the quality of inventory data and the models used for estimation of the state and development of forests can be improved. Methods have been developed for incorporating risk and attitude toward risk in decision analysis, but there has been a lack of good models for dealing with the various sources of risk. The aim of this study was to estimate stochastic models for the variation in growth of Scots pine (Pinus sylvestris), Norway spruce (Picea abies) and birch (Betula pendula and Betula pubescens). The said models had to be capable of generating growth scenarios, and thus correlations between series had to be taken into account. ARMA models were estimated for mean growth index series from Pohjois-Karjala, eastern Finland. Several ARMA models, some of which had seasonal parameters, were found to be adequate for each series. Non-seasonal AR(1) and seasonal AR(1,1) models were used to produce growth scenarios in the case study, in which variation in growth was integrated into forest planning.