How to properly predict data with LSTM using train/test? I see one paradox here. If we use Train/Test split and evaluate our Test data, we might get a good score, but any further prediction will not be credible, because model didn't train the Test data and include it's sequences in memory.

On the other side, we can train the data on Train and Test sequence as train data, but then we can not evaluate our predictions, because we have no testing reference. 

How do you properly predict LSTM models?
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.