This workflow trains and applies an LSTM network to predict energy demand using lagged values of a time series as input. In the Evaluation and Predictions part the trained model is then used for in-sample and out-of-sample forecasting. The forecasted values are compared to the actual values, and the performance of the forecast is reported via scoring metrics and a line plot.