C++ Neural Networks and Fuzzy Logic: Preface

Highlight Features in the Data

For each of the five inputs, we want use a function to highlight rate of change types of features. We will use

the following function (as originally proposed by Jurik) for this purpose.

ROC(n) = (input(t) − BA(t − n)) / (input(t)+ BA(t − n))

where: input(t) is the input’s current value and BA(t − n) is a five unit block average of adjacent values

centered around the value n periods ago.

Now we need to decide how many of these features we need. Since we are making a prediction 10 weeks into

the future, we will take data as far back as 10 weeks also. This will be ROC(10). We will also use one other

rate of change, ROC(3). We have now added 5*2 = 10 inputs to our network, for a total of 15. All of the

preprocessing can be done with a spreadsheet.

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C++ Neural Networks and Fuzzy Logic:Preface

Highlight Features in the Data

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