Whether due to natural causes or human carelessness, forest fires have the power to cause devastating damage, alter the habitat of animals and endemic species, generate insecurity in the population, and even affect human settlements with significant economic losses. These natural and social disasters are very difficult to control, and despite the multidisciplinary human effort, it has not been possible to create efficient mechanisms to mitigate the effects, and they have become the nightmare of every summer season. This study focuses on forecast models for fire measurements using time-series data from the Chilean Ministry of Agriculture. Specifically, this study proposes a comprehensive methodology of deterministic and stochastic time series to forecast the fire measures required by the programs of the National Forestry Corporation (CONAF). The models used in this research are among those commonly applied for time-series data. For the number of fires series, an Autoregressive Integrated Moving Average (ARIMA) model is selected, while for the affected surface series, a Seasonal Autoregressive Integrated Moving Average (SARIMA) model is selected, in both cases due to the lowest error metrics among the models fitted. The results provide evidence on the forecast for the number of national fires and affected national surface measured by a series of hectares (ha). For the deterministic method, the best model to predict the number of fires and affected surface is double exponential smoothing with damped parameter; for the stochastic approach, the best model for forecasting the number of fires is an ARIMA (2,1,2); and for affected surface, a SARIMA$(1,1,0){(2,0,1)}_4$, forecasting results are determined both with stochastic models due to showing a better performance in terms of error metrics.