The inputs from the production set are fed into the neural network model. The neural network model then forecasts the outputs of the production set. The actual outputs of the production set are then compared to the forecasted outputs of the production set. Since the production set is data the neural network model has never seen before, the forecasting of the production set output is a good measure of the accuracy of the neural network model. The next step for building a neural network model is to choose the specific architecture of the neural network.
Many different architectures exist for neural networks.
Certain architectures of neural networks work well for certain forecasting models. Different architectures vary in their computational properties. Much difficulty exists in attempting to determine out which architectures will work well with which type of data. Some general guidelines do exist, and these guidelines were followed to limit the choices of architecture. Various neural network architectures were used to build a model.