How to properly predict data with LSTM using train/test?
You seek to augment the external validity of your model.

The most common way of doing so is by applying k-fold cross-validation to verify that your model generalizes well on unseen data.


  In k-fold cross-validation, the original sample is randomly
  partitioned into k equal sized subsamples. Of the k subsamples, a
  single subsample is retained as the validation data for testing the
  model, and the remaining k − 1 subsamples are used as training data.
  The cross-validation process is then repeated k times, with each of
  the k subsamples used exactly once as the validation data. The k
  results can then be averaged to produce a single estimation.


This will reduce the variance of your model and will reduce its error on unseen data.