In this study, modeling of log production using moving average, single exponential smoothing, double exponential smoothing and winters (multipcaltive and additive) methods and forecasting of monthly log production for 2017, 2018, 2019 and 2020 by means of the highest performing method was aimed. The data used in this study were obtained from the General Directorate of Forestry in Turkey. The data was monthly and included periods 2011-2016. Minitab 16 programme was used for determining best model. Comparisons of models are based on error criteria such as Mean Absolute Deviation (MAD), Mean Absolute Percent Error (MAPE), and Mean Square Deviation (MSD). Forecasts were made by the method, which it has the lowest error criterion values. When the results of accuracy forecasting of applied methods are examined, it was found that winters's multipcaltive-seasonal exponential smoothing method has the highest accuracy forecasting among the obtained methods.