You signed in with another tab or window. Reload to refresh your session.You signed out in another tab or window. Reload to refresh your session.You switched accounts on another tab or window. Reload to refresh your session.Dismiss alert
I'm trying to understand your implementation of predict_sequences_multiple() and especially the way you insert your predictions to the current frame.
As I read your code, the prediction made from the current frame, gets inserted here replacing the last element of the curr_frame array, which makes sense since you are slowly using your previous predictions to make new predictions, and hereby create the ability to predict t+n, rather than t+1.
But I notice that you are inserting all 4 columns values with the predicted y-hat value (bitcoin prize)? (please correct me if I'm wrong about that).
This puzzles me, as I think it would confuse the model to mix the predicted bitcount prize with the 4 features of the dataset (Open, Close, Volume (BTC) and Volume (Currency)) - do you agree?
I've inspected the code several times with the debugger of pycharm, so I'm pretty sure that is what the code is doing, so I guess my question really is why that is the approach you have taken? I understand that the model.predict() method from Keras expects an array of the same shape as it was trained, and you achieve that by doing so, but is this approach "is the way to it", or am I wrong about something?
btw thanks for a great ressource about forecasting with t+n!
Jesper.
The text was updated successfully, but these errors were encountered:
I'm a newbie for multidimensional LSTM networks but what I got is that the goal is looking for patterns in the variation of the Bitcoin price not only from one variable, the price, (or better from one window of values of that dimension) but using one window of values of more dimensions (in this case open, close etc) since there could be correlations between the price and also the variation of those variables
Hi @xcottos, yes your intuition is absolutely correct.
But the question I am raising to @jaungiers implementation is specifically here from L27 to L29 where he inserts the predicted values from the model and appends as both attributes and target values of curr_frame. I'm curious about this approach, as it seems odd to use the predicted bitcoin price as attributes for future predictions when the model originally was trained upon Open, Close etc.
I'm trying to understand your implementation of
predict_sequences_multiple()
and especially the way you insert your predictions to the current frame.As I read your code, the prediction made from the current frame, gets inserted here replacing the last element of the
curr_frame
array, which makes sense since you are slowly using your previous predictions to make new predictions, and hereby create the ability to predict t+n, rather than t+1.But I notice that you are inserting all 4 columns values with the predicted y-hat value (bitcoin prize)? (please correct me if I'm wrong about that).
This puzzles me, as I think it would confuse the model to mix the predicted bitcount prize with the 4 features of the dataset (Open, Close, Volume (BTC) and Volume (Currency)) - do you agree?
I've inspected the code several times with the debugger of pycharm, so I'm pretty sure that is what the code is doing, so I guess my question really is why that is the approach you have taken? I understand that the
model.predict()
method from Keras expects an array of the same shape as it was trained, and you achieve that by doing so, but is this approach "is the way to it", or am I wrong about something?btw thanks for a great ressource about forecasting with t+n!
Jesper.
The text was updated successfully, but these errors were encountered: