I have a dataset like the following one:
Each column is a different numerical feature. Each row represents a timestamp. I want to create an LSTM model that can make prediction of the future time-steps for all the features. For example, I want to use the first 2000 examples to train my model and use the next 1000 to test it. The problem is that I do not know how to proceed.
Since we do not have y's value in this dataset, I was thinking of creating them by shifting the time t+1 to t has a new column y. I explain myself: for example I will have a new column 14, with the value 996.52 for timestamp 0, which is the value at time-step 1 for the feature 0. And so on for all the time-steps and all the features.
The problem is after that I do not know how to feed my LSTM using Keras to make several steps predictions with such a dataset.