03_ARIMA_Models
Solution to the Exercise 3: ARIMA Models This workflow predicts the residual of time series (energy consumption) by autoregressive integrated moving […]
Energy Consumption Forecasting with LSTM
This workflow predicts time series (energy consumption) by an LSTM network with lagged values as input. The trained model is then used for out-of-sample […]
04_Machine_Learning
This workflow predicts the residual of time series (energy consumption) by machine learning models that use lagged values as predictors. The residual of […]
KT_PP-G1
This workflow predicts time series (energy consumption) by an LSTM network with lagged values as input. The trained model is then used for out-of-sample […]
02_TSA_with_LSTM_Network_Deployment
This workflow applies an LSTM network to predict energy demand using lagged values of a time series as input.
01_Additional_Visualizations
Additional plots to visually explore time series data
03_SARIMA_Models
This workflow predicts the residual of time series (energy consumption) by seasonal autoregressive integrated moving average (SARIMA) models that aim at […]
2_ARIMA_Models
This workflow demonstrates how to predict time series (energy consumption) with an autoregressive integrated moving average (ARIMA) model.
Energy Consumption Forecasting with LSTM
This workflow predicts time series (energy consumption) by an LSTM network with lagged values as input. The trained model is then used for out-of-sample […]
02_LSTM_Network
This workflow predicts time series (energy consumption) by an LSTM network with lagged values as input. The trained model is then used for out-of-sample […]