The feed forward neural network (FFANN) model has been the most popular form of artificial neural network model used for forecasting, particularly in economics and finance. In this paper, we elucidate the application of FFANN as a means of modeling financial data. We particularly focus on the model building of FFANN as time series model and use inflation rates in Indonesia as a case study. A comparison is drawn between FFANN model and the best existing models based on traditional econometrics time series approach. The best models are selected on forecasting ability by using the MSE, particularly on the dynamic forecast. The results show that FFANN models outperform the traditional econometric time series model.