I am still finding confusing on look back topic when using LSTM for time-series analysis.
If I have hourly data and I want to predict next 6 hours with multiple predictors, should I look back up to 6 hours when I prepare my training set? 

OR

Should I look back one hour (shift 1) and then predict next hour and take that predicted value and feed it back to predict the value after until next 6 hours? 

This concept is still a little fuzzy for me in LSTM. Any thoughts would be appreciated. 

Assuming your target is (0,1), then the classifier would output a probability matrix of dimension (N,2). 
The first index refers to the probability that the data belong to class 0, and the second  refers to the probability that the data belong to class 1. 

These two would sum to 1.

You can then output the result by:

probability_class_1 = model.predict_proba(X)[:, 1]


If you have k classes, the output would be (N,k), you would have to specify the probability of which class you want.
