Let's assume I am classifying every trading day as a 1 or a 0. Exactly what I am classifying doesn't matter, but for the sake of this question let's say I am predicting direction of price change. So, for a particular stock like GOOG:
For day N:
1: close day N+1 > close day N
0: close day N+1 <= close day N
For every day N, I want to predict whether it is a 1 or 0.
Now let's assume that, as input, I want to use a time series (previous 10 days) of:
- Closing price
- EMA closing price
- Intra-day volatility
That will give me 30 attributes as input. My question, is what makes more sense:
Generate three different classification models (one for each of the above attribute types). Then perform one last classification which will give me a final prediction, which takes the predicted class of each model as input. I.E. This final classification will contain three attributes as input, like [1,0,1] or [1,1,1] etc.
Perform one classification which has all 30 attributes as input.