I am trying to predict Forex time series. The nature of the market is that 80% of the time the price can not be predicted, but in 20% of the time it can be. For example, if the price drops down very deep, there is 99% probability that there will be a recovery process, and this is what I want to predict.
So , how do I train a feed-forward network the way it would only predict those cases that have 99% of certainty to take place and, for the rest of the cases it would output "unpredictable" status?
Imagine that my data set has 24 hours of continuous price data as input (as 1 minute samples), and then as output I want the network to predict 1 hour of future price data. The only restriction I need to implement is that if the network is not "sure" that the price is predictable, it would outupt 0s. So, how do I implement safety in predictions the network is outputting?
It seems that my problem is similar to Google Compose, where it predicts the next word as you are typing , for example, if you type "thank you", it would add " very much" and this would be like 95% correct. I want the same, but it is just that my problem has too much complexity. Google uses RNNs, so maybe I should try a deep network of many layers of RNNs?