Predict multi-steps for Multi-Time series output with Keras
LSTM inputs should be a tensor of size (#samples, time_steps, #features). Considering your dataset; you will have #samples = 2000-1 (for the training); time_steps = 1 (this is the lag-time you choose), and #features = 14. Then for the testing/validation #samples = 1000-1. But here, I suggest you take due consideration to the autocorrelation of the dataset; it seems that you choose the lag-time = 1 (just for the sake of creating the response variables); however, check out the temporal correlation + temporal structure of the data. Similar, to other Deep Neural networks, LSTM requires large dataset to train and test; checkout if you can increase the lag-time and get more predictor data. Have a look at Multi-dimentional and multivariate Time-Series forecast (RNN/LSTM) Keras this similar question for more information.