When creating a neural net to predict tomorrow's close, a five-day simple moving average of the close can be used as an input to the net. This benefits the net in two ways. This is important because noise can obscure the underlying relationships within input data from the network, as it must concentrate on interpreting the noise component. The only disadvantage is that worthwhile information might be lost in an effort to reduce the noise, but this tradeoff always exists when attempting to smooth noisy data. While not all technical indicators have a smoothing effect, this does not mean that they cannot be utilized as data transforms.
Data normalization is the final preprocessing step.
In normalizing data, the goal is to ensure that the statistical distribution of values for each net input and output is roughly uniform. In addition, the values should be scaled to match the range of the input neurons. This means that along with any other transformations performed on network inputs, each input should be normalized as well. Here are three methods of data normalization, the first of which is a simple linear scaling of data. At the very least, data must be scaled into the range used by the input neurons in the neural network.