Combining seasonal time series ARIMA method and neural networks with genetic algorithms for predicting the production value of the mechanical industry in Taiwan

Original Article


Supplying industrial firms with an accurate method of forecasting the production value of the mechanical industry to facilitate decision makers in precise planning is highly desirable. Numerous methods, including the autoregressive integrated-moving average (ARIMA) model and artificial neural networks can make accurate forecasts based on historical data. The seasonal ARIMA (SARIMA) model and artificial neural networks can also handle data involving trends and seasonality. Although neural networks can make predictions, deciding the most appropriate input data, network structure and learning parameters are difficult. Therefore, this article presents a hybrid forecasting method that combines the SARIMA model and neural networks with genetic algorithms. Analytical results generated by the SARIMA model are inputted as the input data of a neural network. Subsequently, the number of neurons in the hidden layer and the number of learning parameters of the neural network architecture are globally optimized using genetic algorithms. This model is subsequently adopted to forecast seasonal time series data of the production value of the mechanical industry in Taiwan. The results presented here provide a valuable reference for decision makers in industry.


Neural network Genetic algorithm Production value 


  1. 1.
    Azoff EM (1994) Neural network time series prediction of financial market. Wiley, LondonGoogle Scholar
  2. 2.
    Box GEP, Jenkins GM (1976) Time series analysis prediction and control. Holden-Day, San FranciscoGoogle Scholar
  3. 3.
    Tang Z, Chrys DA, Fishwick PA (1991) Time series forecasting using neural net works vs. Box-Jenkins methodology. Simulation 57(5):303–331. doi:10.1177/003754979105700508 CrossRefGoogle Scholar
  4. 4.
    Maier HR, Dandy GC (1996) Neural network models for prediction univariate time series. Neural Netw World 6(5):747–772Google Scholar
  5. 5.
    Hibon M, Evgeniou TA (2005) Simple procedure for reliability of repairable systems. Int J Forecast 21:15–24. doi:10.1016/j.ijforecast.2004.05.002 CrossRefGoogle Scholar
  6. 6.
    Rumelhart DE, Hinton GE, Williams RJ (1986) Parallel distributed processing, explorations in the microstructure of cognition, 1, foundations. MIT Press, CambridgeGoogle Scholar
  7. 7.
    Luvai M, Mahmound W (2000) Predictable variation and profitable trading of US equities: a trading simulation using neural networks. Comput Oper Res 27:1111–1129. doi:10.1016/S0305-0548(99)00148-3 MATHCrossRefGoogle Scholar
  8. 8.
    Yu L, Wang S, Lai KK (2005) A novel nonlinear forecasting model incorporating GLAR and ANN for foreign exchange rate. Comput Oper Res 32:2523–2541. doi:10.1016/j.cor.2004.06.024 MATHCrossRefGoogle Scholar
  9. 9.
    Wedding II, Cios KJ (1996) Time series forecasting by combing RBF networks, certainty factors, and the Box-Jenkins model. Neurocomputing 101:49–168Google Scholar
  10. 10.
    Voort MVD, Dougherty M, Watson S (1996) Combing Kohonen maps with ARIMA time series models to forecast traffic flow Transportation Research Part C. Emerg Technol 4:307–318CrossRefGoogle Scholar
  11. 11.
    Juliana Y (2002) A comparison of neural networks with time series models for prediction returns on a stock market index. In: IEA/AIE 2002, LNAI 2358, pp 25–35Google Scholar
  12. 12.
    Zhang GP (2003) Time series forecasting using a hybrid ARIMA and neural network model. Neurocomputing 50:159–175. doi:10.1016/S0925-2312(01)00702-0 MATHCrossRefGoogle Scholar
  13. 13.
    Pai PF, Liu CS (2005) A hybrid ARIMA model and support vector machines neural network model in stock price forecasting. Omega 33:497–505. doi:10.1016/ CrossRefGoogle Scholar
  14. 14.
    Tong LI, Liang YH (2005) Prediction field failure data for repairable systems using neural networks and SARIMA model. Int J Qual Reliab Manage 22(4):410–420. doi:10.1108/02656710510591237 CrossRefGoogle Scholar
  15. 15.
    Holland J (1975) Adaption in natural and artificial systems. University of Michigan Press, Ann ArborMATHGoogle Scholar
  16. 16.
    Adeli H, Hung S (1995) Machine learning: neural networks, genetic algorithms, fuzzy systems. Wiley, New YorkMATHGoogle Scholar
  17. 17.
    Chen SH (2002) Genetic algorithms and genetic programming in computational finance. Kluwer, DordrechtGoogle Scholar
  18. 18.
    Sexton RS, Alidaee B, Dorsey RE, Johnson JD (1998) Global optimization for artificial neural networks: a tabu search application. Eur J Oper Res 106(2/3):570–584. doi:10.1016/S0377-2217(97)00292-0 MATHCrossRefGoogle Scholar
  19. 19.
    Sexton RS, Alidaee B, Dorsey RE, Johnson JD (1998) Toward global optimization of neural networks: a comparison of the genetic algorithm and backpropagation. Decis Support Syst 22(2):171–185. doi:10.1016/S0167-9236(97)00040-7 CrossRefGoogle Scholar
  20. 20.
    Whitley D, Starkweather T, Bogart C (1990) Genetic algorithm and neural networks: optimizing connections and connectivity. Parallel Comput 14:280–311. doi:10.1016/0167-8191(90)90086-O CrossRefGoogle Scholar
  21. 21.
    Bullinaria JA (2007) Using evolution to improve the neural network learning: pitfall and solutions. Neural Comput 16:209–226. doi:10.1007/s00521-007-0087-9 CrossRefGoogle Scholar
  22. 22.
    Kai F, Xu W (1997) Training neural network with genetic algorithms for forecasting the stock price index. In: Proceeding of the 1997 IEEE international conference on intelligent proceeding systems, pp 401–403Google Scholar
  23. 23.
    Shazly MR, Shazly HE (1999) Forecasting exchange rates using a genetically evolved neural network architecture. Int Rev Financ Anal 8:67–82. doi:10.1016/S1057-5219(99)00006-X CrossRefGoogle Scholar
  24. 24.
    Kim KJ, Ham I (2000) Genetic algorithms approach to feature discretization in artificial neural networks for the prediction of stock price index. Expert Syst Appl 19:125–132. doi:10.1016/S0957-4174(00)00027-0 CrossRefGoogle Scholar
  25. 25.
    Liu Y, Yao X (2001) Evolving neural networks for Hang Seng Stock Index Forecast, In: Proceedings of the 2001 congress on evolutionary computation, pp 256–160Google Scholar
  26. 26.
    Paul K, Hoh P, Daohua M, Weidong L (2001) Neural network with genetically evolved algorithms for stocks prediction. Asia-Pacific J Oper Res Singapore 18:103–107Google Scholar
  27. 27.
    Giuliano A, Andrea M, Fabio R (2001) Stock market prediction by a mixture of genetic-neural experts. Int J Pattern Recognit Artif Intell 16(5):501–526Google Scholar
  28. 28.
    Versace M, Bhatt R, Hinds O, Shiffer M (2004) Predicting the exchange traded fund DIA with a combination of genetic algorithms and neural networks. Expert Syst Appl 27:417–425. doi:10.1016/j.eswa.2004.05.018 CrossRefGoogle Scholar
  29. 29.
    Liang YH (2007) Evolutionary neural network modeling for forecasting the field failure data of repairable Systems. Expert Syst Appl 33(4):1090–1096. doi:10.1016/j.eswa.2006.08.032 CrossRefGoogle Scholar
  30. 30.
    Makridakis SR, Andersen A, Carbone R, Fildes R, Hibon M, Lewandowski J, Newton R, Winkler R (1982) The accuracy of extrapolation (time series) methods: results of a forecasting competition. J Forecast 1:111–153. doi:10.1002/for.3980010202 CrossRefGoogle Scholar
  31. 31.
    Terui N, Dijk HK (2002) Combined forecasts from linear and nonlinear time series models. Int J Forecast 18:421–438. doi:10.1016/S0169-2070(01)00120-0 CrossRefGoogle Scholar
  32. 32.
    Fang Y (2003) Forecasting combination and encompassing tests. Int J Forecast 19:87–94. doi:10.1016/S0169-2070(01)00121-2 CrossRefGoogle Scholar
  33. 33.
    Principe JC, Euliano NR, Lefebvre WC (2000) Neural and adaptive systems: fundamentals through simulations. Wiley, New YorkGoogle Scholar
  34. 34.
    Cao Q, Leggio KB, Schniederjans MJ (2005) A comparison between Fama and French’s model and artificial neural networks in predicting the Chinese stock market. Comput Oper Res 32:2499–2512. doi:10.1016/j.cor.2004.03.015 MATHCrossRefGoogle Scholar
  35. 35.
    Delurgio SA (1999) Forecasting principles and applications. Irwin/McGraw-Hill, New YorkGoogle Scholar

Copyright information

© Springer-Verlag London Limited 2008

Authors and Affiliations

  1. 1.Department of Information ManagementI-SHUO UniversityKaohsiung CountryTaiwan, ROC

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