International Journal of Computational Intelligence Systems

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Volume 12, Issue 2, 2019, Pages 1047 - 1061

A Novel Hybrid Autoregressive Integrated Moving Average and Artificial Neural Network Model for Cassava Export Forecasting

Authors
Warut Pannakkong1, Van-Nam Huynh2, *, Songsak Sriboonchitta3
1School of Manufacturing Systems and Mechanical Engineering, Sirindhorn International Institute of Technology, Thammasat University, Pathumthani, Thailand
2School of Knowledge Science, Japan Advanced Institute of Science and Technology, Ishikawa, Japan
3School of Economics, Chiang Mai University, Chiang Mai, Thailand
*Corresponding author. Email: huynh@jaist.ac.jp
Corresponding Author
Van-Nam Huynh
Received 11 April 2019, Accepted 3 September 2019, Available Online 26 September 2019.
DOI
10.2991/ijcis.d.190909.001How to use a DOI?
Keywords
Time series forecasting; Hybrid model; Artificial neural network; ARIMA; Cassava export
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.

Copyright
© 2019 The Authors. Published by Atlantis Press SARL.
Open Access
This is an open access article distributed under the CC BY-NC 4.0 license (http://creativecommons.org/licenses/by-nc/4.0/).

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<Previous Article In Issue
Next Article In Issue>
Journal
International Journal of Computational Intelligence Systems
Volume-Issue
12 - 2
Pages
1047 - 1061
Publication Date
2019/09/26
ISSN (Online)
1875-6883
ISSN (Print)
1875-6891
DOI
10.2991/ijcis.d.190909.001How to use a DOI?
Copyright
© 2019 The Authors. Published by Atlantis Press SARL.
Open Access
This is an open access article distributed under the CC BY-NC 4.0 license (http://creativecommons.org/licenses/by-nc/4.0/).

Cite this article

risenwbib
TY  - JOUR
AU  - Warut Pannakkong
AU  - Van-Nam Huynh
AU  - Songsak Sriboonchitta
PY  - 2019
DA  - 2019/09/26
TI  - A Novel Hybrid Autoregressive Integrated Moving Average and Artificial Neural Network Model for Cassava Export Forecasting
JO  - International Journal of Computational Intelligence Systems
SP  - 1047
EP  - 1061
VL  - 12
IS  - 2
SN  - 1875-6883
UR  - https://doi.org/10.2991/ijcis.d.190909.001
DO  - 10.2991/ijcis.d.190909.001
ID  - Pannakkong2019
ER  -