  Time series forecasting is difficult. It is difficult even for recurrent
neural networks with their inherent ability to learn sequentiality. This
article presents a recurrent neural network based time series forecasting
framework covering feature engineering, feature importances, point and interval
predictions, and forecast evaluation. The description of the method is followed
by an empirical study using both LSTM and GRU networks.
