Pybrain: Completely linear network

Question

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?

Answer 1

尝试将log（）应用于价格属性-然后将所有输入和输出缩放为[-1..1]-当然，当您想从网络输出中获取价格时，必须反转log（）用exp（）