C++ Neural Networks and Fuzzy Logic: Preface

Transform the Data If Appropriate

For time series data, you may consider using a Fourier transform to move to the frequency−phase plane. This

will uncover periodic cyclic information if it exists. The Fourier transform will decompose the input discrete

data series into a series of frequency spikes that measure the relevance of each frequency component. If the

stock market indeed follows the so−called January effect, where prices typically make a run up, then you

would expect a strong yearly component in the frequency spectrum. Mark Jurik suggests sampling data with

intervals that catch different cycle periods, in his paper on neural network data preparation (see references ).

C++ Neural Networks and Fuzzy Logic:Preface

Pre processing the Data for the Network

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You can use other signal processing techniques such as filtering. Besides the frequency domain, you can also

consider moving to other spaces, such as with using the wavelet transform. You may also analyze the chaotic

component of the data with chaos measures. It’s beyond the scope of this book to discuss these techniques.

(Refer to the Resources section of this chapter for more information.) If you are developing short−term

trading neural network systems, these techniques may play a significant role in your preprocessing effort. All

of these techniques will provide new ways of looking at your data, for possible features to detect in other

domains.

Scale Your Data

Neurons like to see data in a particular input range to be most effective. If you present data like the S&P 500

that varies from 200 to 550 (as the S&P 500 has over the years) will not be useful, since the middle layer of

neurons have a Sigmoid Activation function that squashes large inputs to either 0 or +1. In other words, you

should choose data that fit a range that does not saturate, or overwhelm the network neurons. Choosing inputs

from –1 to 1 or 0 to 1 is a good idea. By the same token, you should normalize the expected values for the

outputs to the 0 to 1 sigmoidal range.

It is important to pay attention to the number of input values in the data set that are close to zero. Since the

weight change law is proportional to the input value, then a close to zero input will mean that that weight will

not participate in learning! To avoid such situations, you can add a constant bias to your data to move the data

closer to 0.5, where the neurons respond very well.