A novel hybrid autoregressive integrated moving average and artificial neural network model for cassava export forecasting

Abstract

This paper proposes a novel hybrid forecasting model combining autoregressive integrated moving average (ARIMA) and artificial neural network (ANN) with incorporating moving average and the annual seasonal index for Thailand’s cassava export (i.e., native starch, modified starch, and sago). The comprehensive experiments are conducted to investigate the appropriate parameters of the proposed model as well as other forecasting models compared. In particular, the proposed model is experimentally compared to the ARIMA, the ANN and the other hybrid models according to three popular prediction accuracy measures, namely mean square error (MSE), mean absolute error (MAE) and mean absolute percentage error (MAPE). The empirical results show that the proposed model gives the lowest error in all three measures for the native starch and the modified starch which are major cassava exported products (98% of the total export volume). However, the Khashei and Bijari’s model is the best model for the sago (2% of the total export volume). Therefore, the proposed model can be used as an alternative forecasting method for stakeholders making a decision in cassava international trading to obtain better accuracy in predicting future export of native starch and modified starch which are the majority of the total export.