The present study focuses on creating a forecasting model in order to predict the behavior of the economic indicator known as Gross Fixed Capital Formation (GFCF) of the Brazilian construction industry, which reflects the amount of investment in the construction industry sector. The data set consists of monthly observations from January 1996 to December 2016, the year of 2016 is used as validation for the forecast model. As strong seasonality was identified in the time series, Seasonal Autoregressive Integrated Moving Average (SARIMA) and Holt Winters' models are applied and compared. After the evaluation of the selected models, the ARIMA (2,1,2) × (0,1,1)12 is identified as the best forecast model with reasonable deviations. However, the damped multiplicative Holt Winters' model also produces good results, despite its inability in eliminating the autocorrelation in the residuals. Therefore, both models can predict the GFCF with good accuracy, which can be useful for decision-making by investors and business managers.