Quote from Indrionas:
Hi,
I completely understand what you mean. My input was 200-400 binary variables, derived from OHLC data. And output is a single binary variable (1 indicates probable target behavior tomorrow, 0 indicates "stay out"). I tried both approaches: the "naive" brute-force data mining, and neural network. My experience with neural network is that it either does not converge to the specified accuracy level or overfits to the data and fails miserably in out of sample test.
Another thing to note here is that my inputs and output are binary variables. So it actually does not matter what method of mining you use: brute-force that covers the whole search space, or some "intelligent" heuristic method like neural network or genetic algorithm, that cover only some part of the whole search space.
Based on the above I'd ask the following questions:
1) What NN package were you using? The stuff marketed to the trading community is junk for the most part (NeuroShell, Bioscopy, NuroDimensions, etc). What you want to use is a general purpose corporate modeling applications. These are used successfully every day on very complex tasks: credit car authorizations, oil explorations, climate modeling, national security surveillance.
2) What is you formal education in Computer Science? A BS is good, an MS is better.
3) What is your experience using models to predict other things?
There are lots of formal datasets out there that people use to calibrate and compare modeling strategies or approaches. If you can't get them to generate the published results then likely your usage/method/approach is defective as opposed to the software.
Jerry030