A new hybrid quadratic regression and cascade forward backpropagation neural network

Abstract

In this study, a quadratic regression model (QRM) and a cascade forward backpropagation neural network (CFBN) are jointly integrated together to form a hybrid model called the new hybrid quadratic regression method and cascade forward backpropagation neural (QRM-CFBN) network method. The new hybrid method was tested on a daily time series data obtained from the UCI repository data link and the data set was collected from a combined cycle power plant. The joint integration was made possible by the Bayesian model averaging technique, which was used to obtain a combined forecast from the two separate methods. The model resulting from the joint integration was applied on the log difference series of the original time series data. The results obtained from the new hybrid QRM-CFBN were compared with the results obtained from the hybrid ARIMA-RNN, standalone cascade forward backpropagation neural (CFBN) network and layered recurrent neural network (LRNN) after being tested on the same sample time series data respectively. The comparison indicates that the results emerging from the new hybrid QRM-CFBN method on the average, generally results in better performance when compared with the hybrid ARIMA-RNN, the standalone CFBN and the standalone LRNN for 1day, 3days as well as 5days prediction mean absolute error (MAE) and root mean square error (RMSE) for varying data samples of 50, 100, 200, 400 and 800days respectively. The RMSEs and the MAEs were applied to ascertain the assertion that the new jointly integrated forecast has better forecasting performance greater than the standalone CFBN and LRNN forecasts as well as ARIMA-RNN forecast. The analysis for this study was simulated using MATLAB software, version 8.03.