Timeseries LSTM: does test data need to come after training data?
If I got it correctly, you might have the following scenearios:

labeled samples with tag A or B per date-time index, having as informative attributes the present + some lag values of interest --&gt; (this would be a standard classification approach without time ordering bein necessary)

sliding window of samples (what you mean by subsequences) --&gt; here you should respect the time ordering at least for the validation set, so you make sure you evaluate your LSTM with a real scenario with future never seen sequences. 
With this second approach, you can indeed shuffle the training batches (via the keras shuffle(BUFFER_SIZE).batch(BATCH_SIZE) functionality for instance (info here), but leaving the validation set without shuffling, as follows:
  BATCH_SIZE = 256
  BUFFER_SIZE = 10000

  train_data_multi = tf.data.Dataset.from_tensor_slices((x_train_multi,y_train_multi))
  train_data_multi = train_data_multi.cache().shuffle(BUFFER_SIZE).batch(BATCH_SIZE).repeat()

  val_data_multi = tf.data.Dataset.from_tensor_slices((x_val_multi, y_val_multi))
  val_data_multi = val_data_multi.batch(BATCH_SIZE).repeat()



You can find a complete worked-out example here
You can also make use of the time series data preprocessing helper where you can decide whether to shuffle or not.