Forecasting Malaysia Load Using a Hybrid Model

Abstract

A hybrid model, which combines the seasonal time series ARIMA (SARIMA) and the multilayer feedforward
neural network to forecast time series with seasonality, is shown to outperform both two
single models. Besides the selection of transfer functions, the determination of hidden nodes to use
for the non linear model is believed to improve the accuracy of the hybrid model. In this paper, we
focus on the selection of the appropriate number of hidden nodes on the non linear model to forecast
Malaysia load. Results show that by using only one hidden node, the hybrid model of Malaysia load
performs better than both single models with mean absolute percentage error (MAPE) of less than 1%.