Day-Ahead Forecasting using LSTM with rolling windows

The project involves day-ahead forecasting which takes a model fit into historical data and predicts future demand of electricity. Time series forecasting methods are used as the dataset contains an explicit order dependence between observations, i.e. a time dimension which makes the problem difficult to handle but makes the prediction more accurate. Various machine learning and deep learning algorithms were used to accomplish this work. Results obtained by autoregressive integrated moving average(ARIMA), autoregressive moving average with explanatory variable(ARIMAX), seasonal autoregressive moving average(SARIMA), seasonal autoregressive moving average(SARIMAX) and long short term memory(LSTM) recurrent neural networks with rolling windows were compared and the one with least error, i.e. LSTM with rolling windows was used for time series prediction.