This project aims at researching and implementing a neural network architecture system for the NARX (Nonlinear AutoRegressive with eXogenous inputs) model, used in sequence processing tasks and particularly in time series prediction. The model can fallback to different types of architectures including time-delay neural networks and multi layer perceptron. The NARX simulator tests and compares the different architectures for both synthetic and real data, including the time series of BSE30 index, inflation rate and lake Huron water level. A guideline it's provided for any specialist in the fields of finance, weather forecasting, demography, sales, physics, etc. in order for him to be able to predict and analyze the forecast for any numerical based statistic.