C++ Neural Networks and Fuzzy Logic: Preface

C++ Neural Networks and Fuzzy Logic

by Valluru B. Rao

MTBooks, IDG Books Worldwide, Inc.

ISBN: 1558515526   Pub Date: 06/01/95

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Pre processing the Data for the Network

Surprising as it may sound, you are most likely going to spend about 90% of your time, as a neural network

developer, in massaging and transforming data into meaningful form for training your network. We actually

defined three substeps in this area of preprocessing in our master list:

•  Highlight features

•  Transform

•  Scale and bias

Highlighting Features in the Input Data

You should present the neural network, as much as possible, with an easy way to find patterns in your data.

For time series data, like stock market prices over time, you may consider presenting quantities like rate of

change and acceleration (the first and second derivatives of your input) as examples. Other ways to highlight

data is to magnify certain occurrences. For example, if you consider Central bank intervention as an important

qualifier to foreign exchange rates, then you may include as an input to your network, a value of 1 or 0, to

indicate the presence or lack of presence of Central bank intervention. Now if you further consider the activity

of the U.S. Federal Reserve bank to be important by itself, then you may wish to highlight that, by separating

it out as another 1/0 input. Using 1/0 coding to separate composite effects is called thermometer encoding.

There is a whole body of study of market behavior called Technical Analysis from which you may also wish

to present technical studies on your data. There is a wide assortment of mathematical technical studies that

you perform on your data (see references), such as moving averages to smooth data as an example. There are

also pattern recognition studies you can use, like the “double−top” formation, which purportedly results in a

high probability of significant decline. To be able to recognize such a pattern, you may wish to present a

mathematical function that aids in the identification of the double−top.

You may want to de−emphasize unwanted noise in your input data. If you see a spike in your data, you can

lessen its effect, by passing it through a moving average filter for example. You should be careful about

introducing excessive lag in the resulting data though.