CODEWITHSUNDEEP
pythonneural-networkbackpropagationforecastingpybrain
Momo
Momo

Reputation: 73

Pybrain: Completely linear network

I am currently trying to create a Neural Network with pybrain for stock price forecasting. Up to now I have only used Networks with a binary output. For those Networks sigmoid inner layers were sufficient but I don't think this would be the right approach for Forecasting a price. The problem is, that when I create such a completely linear network I always get an error like

RuntimeWarning: overflow encountered in square while backprop training.

I already scaled down the inputs. Could it be due to the size of my training sets (50000 entries per training set)? Has anyone done something like this before?

Upvotes: 1

Views: 1066

Answers (1)

Fluchtpunkt
Fluchtpunkt

Reputation: 447

Try applying log() to the price-attribute - then scale all inputs and outputs to [-1..1] - of course, when you want to get the price from the network-output you'll have to reverse log() with exp()

Upvotes: 1

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