A Novel Hybrid Autoregressive Integrated Moving Average and Artificial Neural Network Model for Cassava Export Forecasting
Tóm tắt
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.
Tài liệu tham khảo
J.G. De Gooijer, R.J. Hyndman, 25 years of time series forecasting, Int. J. Forecast. 22 (2006), 443–473
Food and Agriculture Organization of the United Nations, The world cassava economy, 2000. http://www.fao.org/3/x4007e/X4007E00.htm#TOC.
C. Kongcharoen, T. Kruangpradit, Autoregressive integrated moving average with explanatory variable (arimax) model for thailand export, in 33rd International Symposium on Forecasting, outh Korea, 2013, pp. 1–8. https://forecasters.org/wp-content/uploads/gravity_forms/7-2a51b93047891f1ec3608bdbd77ca58d/2013/07/Kongcharoen_Chaleampong_ISF2013.pdf
C. Prapasornpittaya, Comparative study on ARIMA, intervention and transfer function models in forecasting Thailand’s export value, Unpublished master’s thesis, Thammasat University, Pathum Thani, Thailand, 2013. https://koha.library.tu.ac.th/cgibin/koha/opac-detail.pl?biblionumber=680825
W. Pannakkong, V.-N. Huynh, S. Sriboonchitta, Arima versus artificial neural network for thailands cassava starch export forecasting, in: V.-N. Huynh, V. Kreinovich, S. Sriboonchitta (Eds.), Causal Inference in Econometrics, 2016, pp. 255–277.
G. Box, G. Jenkins, G. Reinsel, Time Series Analysis: Forecasting and Control, Wiley Series in Probability and Statistics, 2008. https://books.google.co.jp/books?id=lJnnPQAACAAJ.
R.Hecht-Nielsen, Theory of the backpropagation neural network, in International Joint Conference on Neural Networks, IJCNN, IEEE, Washington, 1989, pp. 593–605.
D.C. Park, M. El-Sharkawi, R. Marks, L. Atlas, M. Damborg, Electric load forecasting using an artificial neural network, IEEE Trans. Power Syst. 6 (1991), 442–449
J.-H. Wang, J.-Y. Leu, Stock market trend prediction using arima-based neural networks, in IEEE International Conference on Neural Networks, Washington, 1996, pp. 2160–2165.
G. Zhang, M.Y. Hu, Neural network forecasting of the British pound/us dollar exchange rate, Omega. 26 (1998), 495–506
A. Chaouachi, R.M. Kamel, K. Nagasaka, Neural network ensemble-based solar power generation short-term forecasting, J. Adv. Comput. Intell. Intell. Inform. 14 (2010), 69–75
K.G. Abistado, C.N. Arellano, E.A. Maravillas, Weather forecasting using artificial neural network and bayesian network, J. Adv. Comput. Intell Intell. Inform. 18 (2014), 812–817
G. Zhang, B.E. Patuwo, M.Y. Hu, Forecasting with artificial neural networks: the state of the art, Int. J. Forecast. 14 (1998), 35–62
G.P. Zhang, Time series forecasting using a hybrid arima and neural network model, Neurocomputing. 50 (2003), 159–175
M. Khashei, M. Bijari, A novel hybridization of artificial neural networks and arima models for time series forecasting, Appl. Soft Comput. 11 (2011), 2664–2675
D.Ö. Faruk, A hybrid neural network and arima model for water quality time series prediction, Eng. Appl. Artif. Intell. 23 (2010), 586–594
P.-F. Pai, C.-S. Lin, A hybrid arima and support vector machines model in stock price forecasting, Omega. 33 (2005), 497–505
C.-L.L. Chen-Chun Lin, Hybrid multi-model forecasting system: a case study on display market, Knowl. Based Syst. 71 (2014), 279–289
Y. He, Y. Zhu, D. Duan, Research on hybrid arima and support vector machine model in short term load forecasting, in Sixth International Conference on Intelligent Systems Design and Applications, Jinan, 2006, vol. 1, pp. 804–809.
M. Bouzerdoum, A. Mellit, A.M. Pavan, A hybrid model (sarima–svm) for short-term power forecasting of a small-scale grid-connected photovoltaic plant, Sol. Energy. 98 (2013), 226–235
J. Zhang, Z. Tan, S. Yang, Day-ahead electricity price forecasting by a new hybrid method, Comput. Ind. Eng. 63 (2012), 695–701
M. Shafie-Khah, M.P. Moghaddam, M. Sheikh-El-Eslami, Price forecasting of day-ahead electricity markets using a hybrid forecast method, Energy Convers. Manag. 52 (2011), 2165–2169
N. Chaâbane, A hybrid arfima and neural network model for electricity price prediction, Int. J. Electr. Power Energy Syst. 55 (2014), 187–194
K.-Y. Chen, C.-H. Wang, A hybrid sarima and support vector machines in forecasting the production values of the machinery industry in Taiwan, Expert Syst. Appl. 32 (2007), 254–264
K.-Y. Chen, Combining linear and nonlinear model in forecasting tourism demand, Expert Syst. Appl. 38 (2011), 10368–10376
A. Aslanargun, M. Mammadov, B. Yazici, S. Yolacan, Comparison of arima, neural networks and hybrid models in time series: tourist arrival forecasting, J. Stat. Comput. Simul. 77 (2007), 29–53
J.-H. Lo, A study of applying arima and svm model to software reliability prediction, in 2011 International Conference on Uncertainty Reasoning and Knowledge Engineering (URKE), Bali, 2011, vol. 1, pp. 141–144.
J. Shi, J. Guo, S. Zheng, Evaluation of hybrid forecasting approaches for wind speed and power generation time series, Renew. Sustain. Energy Rev. 16 (2012), 3471–3480
E. Cadenas, W. Rivera, Wind speed forecasting in three different regions of mexico, using a hybrid arima–ann model, Renew. Energy. 35 (2010), 2732–2738
L.A. Díaz-Robles, J.C. Ortega, J.S. Fu, G.D. Reed, J.C. Chow, J.G. Watson, J.A. Moncada-Herrera, A hybrid arima and artificial neural networks model to forecast particulate matter in urban areas: the case of Temuco, Chile, Atmos. Environ. 42 (2008), 8331–8340
S. Barak, S.S. Sadegh, Forecasting energy consumption using ensemble arima–anfis hybrid algorithm, Int. J. Electr. Power Energy Syst. 82 (2016), 92–104
B. Zhu, Y. Wei, Carbon price forecasting with a novel hybrid arima and least squares support vector machines methodology, Omega. 41 (2013), 517–524
J. Ruiz-Aguilar, I. Turias, M. Jimenéz-Come, Hybrid approaches based on sarima and artificial neural networks for inspection time series forecasting, Transp. Res. Part E Log. Trans. Rev. 67 (2014), 1–13
J. Ruiz-Aguilar, I. Turias, M. Jiménez-Come, M. A novel three-step procedure to forecast the inspection volume, Transp. Res. Part C Emer. Technol. 56 (2015), 393–414
K. Jeong, C. Koo, T. Hong, An estimation model for determining the annual energy cost budget in educational facilities using sarima (seasonal autoregressive integrated moving average) and ann (artificial neural network), Energy. 71 (2014), 71–79
C.N. Babu, B.E. Reddy, A moving-average filter based hybrid arima–ann model for forecasting time series data, Appl. Soft Comput. 23 (2014), 27–38
J.E. Dayhoff, Neural Network Architectures: An Introduction, Van Nostrand Reinhold Co., New York, 1990.
I. Kaastra, M. Boyd, Designing a neural network for forecasting financial and economic time series, Neurocomputing. 10 (1996), 215–236
D.J. MacKay, A practical bayesian framework for backpropagation networks, Neural Comput. 4 (1992), 448–472
T. Taskaya-Temizel, M.C. Casey, A comparative study of autoregressive neural network hybrids, Neural Netw. 18 (2005), 781–789
J.D. Cryer, Time Series Analysis (1st ed.), Wadsworth Publ. Co., Belmont, 1986.
K.W. Hipel, A.I. McLeod, Time Series Modelling of Water Resources and Environmental Systems, vol. 45, Elsevier, 1994.