# What to do when facing bias in LSTM?

I have created an LSTM model which is trained on an 8-hour time frame for a cryptocurrency. When the training is finished I see that it is learning the pattern but there is some bias in it. How to handle this?

This is my model:

'''Build LST model'''
model = Sequential()

• Sorry but it's practically impossible that the model would predict so accurately a non-periodic curve. I think there is a confusion, are you sure you used the predict() method to generate this graph? I don't know exactly what is happening but this doesn't look at all like a realistic prediction on a test set. My guess is that it shows the curve of the validation set when the validation data hasn't been correctly specified and is picked randomly instead (the default). Jun 24 at 13:44