Recent content by Dmitry Lukovkin

There're some methods for it, see Input Variable Selection. There's a comprehensive review - http://cdn.intechopen.com/pdfs/14882.pdf

Not yet, do you think it's worth adding? Does it have a great sense for individual stocks or is it better to let's say construct some portfolios and track VAMI for them? Your opinion is greatly appreciated.

I'm working on this subject for a while, experimenting with different architectures and models. When I started to obtain more or less satisfying results, I've tested them on my own account, rather successfully. After that I decided to publish predictions using website - http://stocksneural.net...

If you have linear activation on the output layer and linear dependency of output on input (on your your chart it just y = x), then even 1 neuron NN should catch it up. If the output activation is a sigmoid, there will be errors.

In other words, you are trying to fit one sigmoid (or tanh, or something) to all of your input data. It makes sense only in the case if your target deltas behave like a sigmoid function of your input prices.

1 neuron will not be able to generalize on anything complex. You're just creating non-linear scalar function which will look like W_h_o * sigmoid(W_i_h * x + b_i_h) + b_h_o, which takes only one scalar x as input. Let's say, input price is 100. What be the delta in 5 days? We don't know anything...

A bit lack of information regarding NN architecture and hyperparameters. Let's say you have X_t-n, X_t-n+1,... X_t-2, X_t-1, X_t as inputs. If you want to predict Delta on 5 days, the target output during training should be (X_t+5 - X_t), not the input sequence. Also pay attention to the...