This thesis attempts to predict daily number of firefighter incidents in the Central Bohemia Region and in the Region of Hradec Králové to improve firefighter shift planning. The analysis is based on a dataset of firefighter incidents from the period between the years 2008 and 2012. Econometric models, capturing yearly and weekly patterns and weather impact were estimated and used for long-term prediction. The first part of the thesis provides a description of tests applied to residuals and other econometric tests used in this study. Then linear regression is applied to model weather impact and effects of days of week and months of year. In the next part regression with AR errors, (S)ARMA models and regression with (S)ARMA errors are estimated. All these models are compared according to properties of residuals and out-of-sample mean absolute percentage error (MAPE). The most accurate models predict daily number of incidents two months ahead with MAPE slightly above 20% which is considerably better than the benchmark Holt-Winters method. Regression models with (S)ARMA errors produce relatively accurate long-term forecasts and its error terms are uncorrelated. Therefore, they can be considered suitable for long-term prediction of firefighter incidents.