I'm seeing this tutorial to know how to use LSTM to predict time series data and I noticed that he shifted the target/labels up so that the features are all in time t but the target is t+1
so my simple question is, should we always do this when working with time series data?
I have a time series data and I want to use it to build a regression model with RNN. I used feed forward NN first but it didn't work good enough so I decided to use RNN/LSTM. My question is should I let the dataset as it is now where every row have features and target at time t or should I shift the target column so that the features would always be at time t but the target is always shifted(t+1) ahead?