A new hybrid genetic algorithm-sarima-artificial neural network in forecasting Malaysian export amount of palm oil

Chai, Kah Chun (2021) A new hybrid genetic algorithm-sarima-artificial neural network in forecasting Malaysian export amount of palm oil. Masters thesis, Universiti Tun Hussein Malaysia.


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Malaysia is a significant export country of palm oil to all over the world. Therefore, forecasting of palm oil export is required to help in boosting the nation’s socioeconomic development as well as for the plantation companies to sustain and improve for a better management regarding export. The traditional Seasonal Autoregressive Integrated Moving Average (SARIMA) model assumes that all the parameters in the non-seasonal and seasonal parameters are significant which will lead to inaccuracy in the model identification stage and increase the cost of reidentification. Hence, this study aimed to optimise the order of subset of the SARIMA model using Genetic Algorithm (GA). It would be then combined with Artificial Neural Network (ANN) to form a novel hybrid GA-SARIMA-ANN to predict Malaysian palm oil export. The performance of the proposed hybrid GASARIMA-ANN was compared with four existing models, which were SARIMA, ANN, hybrid GA-SARIMA, and hybrid SARIMA-ANN. The forecast accuracy for all the models was evaluated using Mean Absolute Error (MAE), Root Mean Square Error (RMSE), Mean Absolute Percentage Error (MAPE), and Pearson correlation coefficient. The empirical result showed that the proposed hybrid GASARIMA-ANN (5-6-1) yielded the lowest forecasting accuracy where its MAPE was only 8.15% compared with the existing models, whose MAPE values were slightly above 10%. In addition, the proposed hybrid model achieved the highest correlation coefficient, higher by 27% on average compared with the benchmark models. It is proved that GA facilitates the model identification for SARIMA, while the coupling of ANN may help to model nonlinearity, making the hybrid model’s forecast more accurate.

Item Type: Thesis (Masters)
Subjects: H Social Sciences > HD Industries. Land use. Labor > HD9000-9999 Special industries and trades > HD9000-9495 Agricultural industries
Divisions: Faculty of Applied Science and Technology > Department of Physics and Chemistry
Depositing User: Mrs. Sabarina Che Mat
Date Deposited: 05 Feb 2022 07:19
Last Modified: 05 Feb 2022 07:19
URI: http://eprints.uthm.edu.my/id/eprint/6292

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