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A Hybrid Model of Differential Evolution with Neural Network on Lag Time Selection for Agricultural Price Time Series Forecasting

  • Chen ZhiYuanEmail author
  • Le Dinh Van Khoa
  • Lee Soon Boon
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10645)

Abstract

The contribution of time series forecasting (TSF) on various aspects from economic to engineering has yielded its importance. Lot of recent studies concentrated on applying and modifying artificial neural network (ANN) to improve forecasting accuracy and achieved promising results. However, the selection of proper set from historical data for forecasting still has limited consideration. In addition, the selection of network structure as well as initial weights in ANN has been proved to have significant impact on the performance. This paper aims to propose a hybrid model that takes advantages of optimization algorithm: differential evolution (DE) in combine with ANN. The DE operates as features selection process that evaluates useful historical data known as lag to involve in learning process. Besides, DE will perform pre-calculation to determine the set of weight use for ANN. This proposed model is examined on agricultural commodity’s price to evaluate its accuracy. The experimental results is compared and surpassed the popular TSF technique autoregressive integrated moving average (ARIMA) and traditional multilayer perceptron (MLP).

Keywords

Time series forecasting Artificial neural network Differential evolution Lag time selection 

References

  1. 1.
    Bontempi, G., Ben Taieb, S., Le Borgne, Y.-A.: Machine learning strategies for time series forecasting. In: Aufaure, M.-A., Zimányi, E. (eds.) eBISS 2012. LNBIP, vol. 138, pp. 62–77. Springer, Heidelberg (2013). doi: 10.1007/978-3-642-36318-4_3 CrossRefGoogle Scholar
  2. 2.
    Broersen, P.M.T.: The quality of lagged products and autoregressive Yule-Walker models as autocorrelation estimates. IEEE Trans. Instrum. Meas. 58, 3867–3873 (2009). doi: 10.1109/TIM.2009.2021206 CrossRefGoogle Scholar
  3. 3.
    Rahman, S.A., Huang, Y., Claassen, J., Kleinberg, S.: Imputation of missing values in time series with lagged correlations. In: IEEE International Conference on Data Mining Workshops, ICDMW, pp. 753–762 (2015)Google Scholar
  4. 4.
    Araujo, R.D.A, Junior, A.R.L., Ferreira, T.A.E.: Morphological-rank-linear time-lag added evolutionary forecasting method for financial time series forecasting. In: 2008 IEEE Congress on Evolutionary Computation, CEC 2008, pp. 1340–1347 (2008)Google Scholar
  5. 5.
    De Oliveira, J.F.L., Ludermir, T.B.: A hybrid evolutionary system for parameter optimization and lag selection in time series forecasting. In: Proceedings - 2014 Brazilian Conference on Intelligent Systems, BRACIS 2014, pp. 73–78 (2014)Google Scholar
  6. 6.
    Wong, W., Bai, E., Chu, A.W.: Adaptive time-variant models for fuzzy-time-series forecasting. IEEE Trans. Syst. Man Cybern. Part B Cybern. 40, 1531–1542 (2010)CrossRefGoogle Scholar
  7. 7.
    Cortez, P.: Sensitivity analysis for time lag selection to forecast seasonal time series using neural networks and support vector machines. In: The 2010 International Joint Conference on Neural Networks (IJCNN), pp. 1–8 (2010)Google Scholar
  8. 8.
    Zhang, G.P.: Time series forecasting using a hybrid ARIMA and neural network model. Neurocomputing. 50, 159–175 (2003). doi: 10.1016/S0925-2312(01)00702-0 CrossRefzbMATHGoogle Scholar
  9. 9.
    Ömer Faruk, D.: A hybrid neural network and ARIMA model for water quality time series prediction. Eng. Appl. Artif. Intell. 23, 586–594 (2010). doi: 10.1016/j.engappai.2009.09.015 CrossRefGoogle Scholar
  10. 10.
    Khashei, M., Bijari, M.: A new class of hybrid models for time series forecasting. Expert Syst. Appl. 39, 4344–4357 (2012). doi: 10.1016/j.eswa.2011.09.157 CrossRefGoogle Scholar
  11. 11.
    Jain, A., Kumar, A.M.: Hybrid neural network models for hydrologic time series forecasting. Appl. Soft Comput. J. 7, 585–592 (2007). doi: 10.1016/j.asoc.2006.03.002 CrossRefGoogle Scholar
  12. 12.
    Rather, A.M., Agarwal, A., Sastry, V.N.: Recurrent neural network and a hybrid model for prediction of stock returns. Expert Syst. Appl. 42, 3234–3241 (2015). doi: 10.1016/j.eswa.2014.12.003 CrossRefGoogle Scholar
  13. 13.
    Rivero, C.R., Pucheta, J., Laboret, S., Herrera, M., Sauchelli, V.: Method: application to cumulative rainfall. IEEE Trans. Lat. Am. Trans. 11, 359–364 (2013)CrossRefGoogle Scholar
  14. 14.
    Araujo, R.DA., Vasconcelos, G.C., Ferreira, T.A.E.: Hybrid differential evolutionary system for financial time series forecasting. In: 2007 IEEE Congress on Evolutionary Computation, pp. 4329–4336 (2007)Google Scholar
  15. 15.
    Araújo, R.D.A., Oliveira, A.L.I., Meira, S.: A hybrid model for high-frequency stock market forecasting. Expert Syst. Appl. 42, 4081–4096 (2015). doi: 10.1016/j.eswa.2015.01.004 CrossRefGoogle Scholar
  16. 16.
    Ribeiro, G.H.T., de M. Neto, P.S.G., Cavalcanti, G.D.C., Tsang, I.R.: Lag selection for time series forecasting using particle swarm optimization. In: International Joint Conference on Neural Networks, pp. 2437–2444 (2011)Google Scholar
  17. 17.
    Cai, X., Zhang, N., Venayagamoorthy, G.K., Wunsch, D.C.: Time series prediction with recurrent neural networks trained by a hybrid PSO-EA algorithm. Neurocomputing. 70, 2342–2353 (2007). doi: 10.1016/j.neucom.2005.12.138 CrossRefGoogle Scholar
  18. 18.
    Brasileiro, R.C., Souza, V.L.F., Fernandes, B.J.T., Oliveira, A.L.I.: Automatic method for stock trading combining technical analysis and the Artificial Bee Colony Algorithm. In: 2013 IEEE Congress on Evolutionary Computation, CEC 2013, pp. 1810–1817 (2013)Google Scholar
  19. 19.
    Huang, G., Wang, L.: Hybrid neural network models for hydrologic time series forecasting based on genetic algorithm. In: 2011 Fourth International Joint. Conference Computational Science Optimization, pp. 1347–1350 (2011). doi: 10.1109/CSO.2011.147
  20. 20.
    Parras-Gutierrez, E., Rivas Santos, V.: Time series forecasting: Automatic determination of lags and radial basis neural networks for a changing horizon environment. In: International Joint Conference on Neural Networks IJCNN, pp. 1–7 (2010)Google Scholar
  21. 21.
    Mohammadi, R., Fatemi Ghomi, S.M.T., Zeinali, F.: A new hybrid evolutionary based RBF networks method for forecasting time series: a case study of forecasting emergency supply demand time series. Eng. Appl. Artif. Intell. 36, 204–214 (2014). doi: 10.1016/j.engappai.2014.07.022 CrossRefGoogle Scholar
  22. 22.
    Vesterstrom, J., Thomsen, R.: A comparative study of differential evolution, particle swarm optimization, and evolutionary algorithms on numerical benchmark problems. In: 2004 Congress on Evolutionary Computation, CEC2004, pp. 1980–1987 (2004)Google Scholar
  23. 23.
    Neri, F., Tirronen, V.: Recent advances in differential evolution: a survey and experimental analysis. Artif. Intell. Rev. 33, 61–106 (2010)CrossRefGoogle Scholar
  24. 24.
    Segura, C., Coello Coello, C.A., Hernández-Díaz, A.G.: Improving the vector generation strategy of Differential Evolution for large-scale optimization. Inf. Sci. (Ny) 323, 106–129 (2015). doi: 10.1016/j.ins.2015.06.029 CrossRefMathSciNetGoogle Scholar
  25. 25.
    Wang, L., Zeng, Y., Chen, T.: Back propagation neural network with adaptive differential evolution algorithm for time series forecasting. Expert Syst. Appl. 42, 855–863 (2015). doi: 10.1016/j.eswa.2014.08.018 CrossRefGoogle Scholar
  26. 26.
    Peralta Donate, J., Cortez, P.: Evolutionary optimization of sparsely connected and time-lagged neural networks for time series forecasting. Appl. Soft Comput. 23, 432–443 (2014). doi: 10.1016/j.asoc.2014.06.041 CrossRefGoogle Scholar
  27. 27.
    Storn, R., Price, K.: Differential evolution – a simple and efficient heuristic for global optimization over continuous spaces. J. Glob. Optim. 11, 341–359 (1997)CrossRefzbMATHMathSciNetGoogle Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Chen ZhiYuan
    • 1
    Email author
  • Le Dinh Van Khoa
    • 1
  • Lee Soon Boon
    • 1
  1. 1.The University of Nottingham Malaysia CampusSemenyihMalaysia

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