How to train and predict Keras LSTM with time series data?

Question

I have figured out how to train a LSTM using just values, but what would the data look like if I wanted to include the time? Perhaps input dimension of 2, with time as epoch seconds and normalized values? There may be time gaps in the data and I want the training to reflect that.

Assuming I only want to periodically train the LSTM, since this is an expensive operation, how would you predict values in the future with a gap between the last training time and the first predicted time? For example, lets says I trained the LSTM 3 days ago, but now I want to predict the values for the next day.

All my work so far is based on this article: http://machinelearningmastery.com/time-series-prediction-lstm-recurrent-neural-networks-python-keras/ . But it doesn't cover these kinds of questions.

Answer 1

I think you can handle this situation when constructing your training set, at least if the time delay between the last value (in the input sequence) and the value to predict is fixed.

Let X_train have dimension: (nb_samples, timesteps, input_dim) and y_train have dimension (n_samples, output_dim). Let x be one training input sample. It corresponds to a multivariate time series with dimension (timesteps, input_dim). Its corresponding output is y with dimension (output_dim).

In y you put the value to predict which can be 3 days after the last value in x, the LSTM "should" grasp the temporal dependency. So if the time delay between the last value in the input and the value to predict is fixed, this should work.

That was the case for such a problem: https://challengedata.ens.fr/en/challenge/9/prediction_of_transaction_volumes_in_financial_markets.html