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HomeAdvanced Materials ResearchAdvanced Materials Research Vol. 845Evaluating ARIMA-Neural Network Hybrid Model...

Evaluating ARIMA-Neural Network Hybrid Model Performance in Forecasting Stationary Timeseries

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Abstract:

Demand prediction is one of most sophisticated steps in planning and investments. Although many studies are conducted to find the appropriate forecasting models, dynamic nature of forecasted parameters and their effecting factors are apparent evidences for continuous researches. ARIMA, Artificial Neural Network (ANN), and ARIMA-ANN hybrid model are well-known forecasting models. Many researchers concluded that the Hybrid model is the predominant forecasting model in comparison with ARIMA and ANN individual models. Most of these researches are based on non-stationary or seasonal timeseries, whereas in this article, hybrid models forecast ability by stationary time series is studied. Some following demand time steps from a paint manufacturing company are forecasted by all previously mentioned models and ARIMA-ANN hybrid model fails to present the best forecasts.

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Materials, Industrial, and Manufacturing Engineering Research Advances 1.1

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Periodical:

Advanced Materials Research (Volume 845)

Pages:

510-515

DOI:

https://doi.org/10.4028/www.scientific.net/AMR.845.510

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Online since:

December 2013

Authors:

Seyed Navid Seyedi*, Pouyan Rezvan, Saeed Akbarnatajbisheh, Syed Ahmad Helmi

Keywords:

Arima, Artificial Neural Network (ANN), Forecasting, Hybrid Models, Timeseries Forecasting

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© 2014 Trans Tech Publications Ltd. All Rights Reserved

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[1] Z.L. Sun, T.M. Choi, K.F. Au, Y. Yu, Sales forecasting using extreme learning machine with applications in fashion retailing, Decision Support Systems. 46 (2008) 411-419.

DOI: 10.1016/j.dss.2008.07.009

[2] B. L. Bowerman, R. T. O'Connell, Time series forecasting. Duxbury, Boston. (1987).

[3] S. Chopra, P. Meindl, SUPPLY CHAIN MANAGEMENT, PEARSON. (2010) 198-226.

[4] G. P. Zhang, Time series forecasting using a hybrid ARIMA and neural network model, Neurocomputing. 50 (2003) 159-175.

DOI: 10.1016/s0925-2312(01)00702-0

[5] V.S. Ediger, S. Akar, ARIMA forecasting of primary energy demand by fuel in Turkey, Energy Policy. 35 (2007) 1701-1708.

DOI: 10.1016/j.enpol.2006.05.009

[6] M. Z. Babai, M. M. Ali, J. E. Boylan, A. A. Syntetos, Forecasting and inventory performance in a two-stage supply chain with ARIMA (0, 1, 1) demand: Theory and empirical analysis, International Journal of Production Economics (2011).

DOI: 10.1016/j.ijpe.2011.09.004

[7] B. F. Francesco Virili, Nonstationarity and Data Preprocessing for Neural Network Predictions of an Economic Time Series, In Proceedings ofIEEE-INNS-ENNS International Joint Conference on Neural Networks (IJCNN'00). 5 (2000) 5129-5136.

DOI: 10.1109/ijcnn.2000.861446

[8] M. Khashei, M. Bijari, A novel hybridization of artificial neural networks and ARIMA models for time series forecasting, Applied Soft Computing. 11 (2011) 2664-2675.

DOI: 10.1016/j.asoc.2010.10.015

[9] L. A. Diaz-Robles, J. C. Ortega, J. S. Fu, G. D. Reed, J. C. Chow, A hybrid ARIMA and artificial neural networks model to forecast particulate matter in urban areas: The case of Temuco, Chile. Atmospheric Environment. 42 (2008) 8331-8340.

DOI: 10.1016/j.atmosenv.2008.07.020

[10] A.P. Ansuj, M.E. Camargo, R. Radharamanan, D.G. Petry, Sales forecasting using time series and neural networks, Computers and Industrial Engineering. 31 (1996) 421–424.

DOI: 10.1016/0360-8352(96)00166-0

[11] F. C Palm, A. Zellner, To combine or not to combine? Issues of combining forecasts, Journal of Forecasting. 11 (1992) 687-701.

DOI: 10.1002/for.3980110806

[12] J. M. Bates, C. W. J. Granger, The combination of forecasts. (1969) 451-468.

[13] S. Hashem, Optimal linear combinations of neural networks, Purdue University. Ph.D. (1993).

[14] S. Hashem, B. Schmeiser, Improving model accuracy using optimal linear combinations of trained neural networks. Neural Networks, IEEE Transactions. 6 (1995) 792-794.

DOI: 10.1109/72.377990

[15] R. R. Andrawis, A. F. Atiya, H. El-Shishini, Forecast combinations of computational intelligence and linear models for the NN5 time series forecasting competition. International Journal of Forecasting. 27 (2011) 672-688.

DOI: 10.1016/j.ijforecast.2010.09.005

[16] J. S. Armstrong. Principles of forecasting: a handbook for researchers and practitioners, Springer. (2001).

[17] J. C. Gutiérrez-Estrada, C. Silva, E. Yáñez, N. Rodríguez, I. Pulido-Calvo, Monthly catch forecasting of anchovy Engraulis ringens in the north area of Chile: Non-linear univariate approach, Fisheries Research. 86. 2 (2007) 188-200.

DOI: 10.1016/j.fishres.2007.06.004

[18] H. Liu, H. Q. Tian, Y. F. Li, Comparison of two new ARIMA-ANN and ARIMA-Kalman hybrid methods for wind speed prediction, Applied Energy. (2012).

DOI: 10.1016/j.apenergy.2012.04.001

[19] S. De Leeuw, K. V. Donselaar, T. D. Kok, Forecasting techniques in logistics, Advances in distribution logistics, Springer Berlin Heidelberg. (1998) 481-499.

DOI: 10.1007/978-3-642-46865-0_20

[20] S. Makridakis, M. Hibon, The M3-Competition: results, conclusions and implications, International journal of forecasting. 16 (2000) 451-476.

DOI: 10.1016/s0169-2070(00)00057-1

[21] J. Shi, J. Guo, S. Zheng, Evaluation of hybrid forecasting approaches for wind speed and power generation time series, Renewable and Sustainable Energy Reviews. 16 (2012) 3471-3480.

DOI: 10.1016/j.rser.2012.02.044

[22] T. Taskaya-Temizel, M. C. Casey, A comparative study of autoregressive neural network hybrids, Neural Networks. 18 (2005) 781-789.

DOI: 10.1016/j.neunet.2005.06.003

[23] T. Taskaya-Temizel, K. Ahmad. Are ARIMA neural network hybrids better than single models?, Neural Networks. (2005).

DOI: 10.1109/ijcnn.2005.1556438

[24] G. Box, G. Jenkins, Time series analysis; forecasting and control. Holden-Day, San Francisco(CA). (1970).

[25] G. P. Zhang, M. Qi, Neural network forecasting for seasonal and trend time series, European journal of operational research. 160 (2005) 501-514.

DOI: 10.1016/j.ejor.2003.08.037

[26] W. C. Hong, Y. Dong, L. Y. Chen, S. Y. Wei, SVR with hybrid chaotic genetic algorithms for tourism demand forecasting, Applied Soft Computing. 11 (2011) 1881-1890.

DOI: 10.1016/j.asoc.2010.06.003

[27] P. J. Werbos, Generalization of backpropagation with application to a recurrent gas market model. Neural networks. 1 (1988) 339-356.

DOI: 10.1016/0893-6080(88)90007-x