If you think the best answer to predicting things is to just throw a neural net at it, you should probably learn more control theory. :)

(Less flippantly - neural nets are mainly for the case where you can't effectively do manual feature engineering because your inputs are too varied and complex, you don't particularly care about the mathematical properties or guarantees of the solution, and where you don't even really know what the solution should look like [so are forced to randomly initialize a huge nonlinear system and optimize it until useful behaviors pop out]. Something like predicting path saturation would be far better suited to traditional approaches.)