Elman Neural Networks
by: Alfonsus J. Endharta
Neural Network (NN) is one of the methods mostly used for forecasting non-linear data in many countries. NN which is mostly used id many researches is Feed-Forward Neural Network (FFNN) or Autoregressive Neural Network (AR-NN). AR-NN is not able to catch and represent moving average (MA) order effects in time series data. This research is done to study another NN type application, i.e. Elman-Recurrent Neural Network (Elman-RNN) which is able to represent MA order, and to com-pare the forecast accuracy with Seasonal Autoregressive Integrated Moving Average (SARIMA) mo-del. Maximum and minimum temperature data from BMG Meteorology Station Kalianget is used as the case study. The analysis results that the best ARIMA model for forecasting short-term maximum temperature is ARIMA([1, 19], 1, 1)(0, 0, 1)365 and ARIMA([1, 6, 14], 1, [1, 5, 11])(1, 0, 0)365 for the minimum temperature data. Both model give white noise residuals, but they don’t follow the normal distribu-tion. Elman-RNN inputs which is studied and applied for forecasting the case study data are ARIMA lag inputs. Therefore, there are three nets built for each maximum and minimum temperature data. This net use one hidden layer with tangent sigmoid function and one output with linear function. The forecast accuracy comparison based on out-sample MAPE value shows that the best net for the maximum temperature data is Elman-RNN(4, 5, 1) and Elman-RNN(11, 9, 1) for the minimum tempe-rature data. Those nets are the best model for forecasting maximum and minimum temperature in BMG Meteorology Station Kalianget compared with SARIMA model.
Source: Alfonsus J. Endharta
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