Input Data Selection and Preprocessing Neural network modeling is based upon the developer's understanding of the underlying real-world relationship between the inputs and the output. Decisions must be made about what is to be predicted and what data will be used as inputs in the network. Knowledge of the financial markets, coupled with the use of various tools such as principal component analysis to find correlations between related markets, is necessary for appropriate input data selection. Once input data has been selected, it must be preprocessed. By reducing the number of inputs to the network, preprocessing helps it learn more easily.
Transformation manipulates raw data inputs to create a single input to a net.
Normalization transforms a single data input to distribute the data evenly and scale the data to match the range of the input neurons. In most neural network applications, transformation involves algebraic or statistical manipulation of the input data. In financial forecasting applications, a wide variety of technical indicators, commonly used by traders to interpret market behavior, can be used as transforms. Preprocessed inputs might include differences, ratios, and moving averages of the open, high, low, close, volume, and open interest raw data. Each neuron in the input layer represents one of the preprocessed inputs.