Progress Report on Neural Net

Well, reducing the number of nodes in the hidden layer didn't help much; if anything it made things look slightly more erratic. As a result I decided to increase the number of input features to 102, which gave much more pleasing results. A screen shot of this newer NN, in the bottom pane, is shown below

Comparing this with the earlier version shown in my previous post, for example by looking at the smooth uptrend in the middle, it can be seen that there are far fewer "false" market types indicated - a definite improvement. The moral seems to be that adding more informative features is the way to go.

However, this raises the problem of training time - it took about 30 hours to train this model using my current Octave scripts - which is far too long for me. Due to this I have decided to use the FANN library, fanntool and the octave-fann bindings for my future development of NNs. I've recently been playing around with these and I think that, in the long run, a lot of time will be saved, even though I will have to write a certain amount of "glue code" to achieve what I want. The above 102 input feature NN will be my reserve NN in the event that I can't get the FANN library, fanntool and octave-fann to work to my satisfaction.