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Symbolic Regression for prediction and forecasting... Love to hear your thoughts...


In forecasting and in commercial/sme risk scoring there is a need for trying vast number of algebraic equations which is a very cumbersome prosess. Let's add symbolic regression as a new competitive capability.



Summations, ratios, power transforms and all combinations of a like are needed to be tested as new variables for a forecasting or prediction model. Doing this by hand manually is a though and long business... And there is always a possibility for one to skip a valuable combination.



Symbolic regression is a novel techinique for automatically generating algebraic equations with use of genetic programming
In every evolution a variable is selected checked if the equation is discriminatitive of the target variable at hand. In every next step frequently observed variables will be selected more likely.


SR comparison with linear regression neural nets and random forestsSR comparison with linear regression neural nets and random forests


Benefit for clients:

This method produces variables mainly with nonlinear relationships. It is a technique that will help in corporate/commercial/sme risk modelling, such that powerful risk models are generated from a hort list of B/S and P/L based algebraic equations.
There is potential use cases in algorithmic trading as well...


There are 3 very interesting world problems solved with symbolic regression here.

A very relevant thesis by sean Wouter is attached as a pdf document for your reading pleasure...


R side of things:

I've found Rgp package for genetic programming, here is a link.



I haven't seen something similar in SAS, SPSS but there is this;

Also there is Bruce Ratner's page


Status changed to: Inactive

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