Online DCA for Times Series Forecasting Using Artificial Neural Network

  • Viet Anh NguyenEmail author
  • Hoai An Le Thi
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 991)


In this work, we study the online time series forecasting problem using artificial neural network. To solve this problem, different online DCAs (Difference of Convex functions Algorithms) are investigated. We also give comparison with online gradient descent—the online version of one of the most popular optimization algorithm in the collection of neural network problems. Numerical experiments on some benchmark time series datasets validate the efficiency of the proposed methods.


Online DCA DC programming DCA Time series forecasting Artificial neural network 


  1. 1.
    Anders, U., Korn, O., Schmitt, C.: Improving the pricing of options: a neural network approach. J. Forecast. 17(5–6), 369–388 (1998)CrossRefGoogle Scholar
  2. 2.
    Box, G.E., Jenkins, G.M., Reinsel, G.C., Ljung, G.M.: Time series analysis: forecasting and control. Wiley (2015)Google Scholar
  3. 3.
    Ho, V.T., Le Thi, H.A., Bui Dinh, C.: Online DC optimization for online binary linear classification. In: Nguyen, N.T., Trawiński, B., Fujita, H., Hong, T.P. (eds.) Intelligent Information and Database Systems, pp. 661–670. Springer, Berlin (2016)Google Scholar
  4. 4.
    Hornik, K.: Some new results on neural network approximation. Neural Netw. 6(8), 1069–1072 (1993)CrossRefGoogle Scholar
  5. 5.
    Hornik, K., Stinchcombe, M., White, H.: Multilayer feedforward networks are universal approximators. Neural Netw. 2(5), 359–366 (1989)CrossRefGoogle Scholar
  6. 6.
    Kantz, H., Schreiber, T.: Nonlinear Time Series Analysis, vol. 7. Cambridge University Press (2004)Google Scholar
  7. 7.
    Le Thi, H.A., Pham Dinh, T.: The DC (Difference of Convex functions) programming and DCA revisited with DC models of real world nonconvex optimization problems. Ann. Oper. Res. 133(1), 23–46 (2005)MathSciNetzbMATHGoogle Scholar
  8. 8.
    Le Thi, H.A., Pham Dinh, T.: DC programming and DCA: thirty years of developments. Math. Program. 169(1), 5–68 (2018)MathSciNetCrossRefGoogle Scholar
  9. 9.
    LeCun, Y., Bengio, Y., Hinton, G.: Deep learning. Nature 521(7553), 436–444 (2015)CrossRefGoogle Scholar
  10. 10.
    Li, Y., Yuan, Y.: Convergence analysis of two-layer neural networks with ReLU activation. In: Advances in Neural Information Processing Systems, pp. 597–607 (2017)Google Scholar
  11. 11.
    Medeiros, M.C., Teräsvirta, T., Rech, G.: Building neural network models for time series: a statistical approach. J. Forecast. 25(1), 49–75 (2006)MathSciNetCrossRefGoogle Scholar
  12. 12.
    Pan, X., Srikumar, V.: Expressiveness of rectifier networks. In: Proceedings of the 33rd International Conference on International Conference on Machine Learning. ICML’16, vol. 48, pp. 2427–2435. (2016)Google Scholar
  13. 13.
    Pham Dinh, T., Le Thi, H.A.: Convex analysis approach to d.c. programming: theory, algorithm and applications. Acta Math. Vietnam. 22(01) (1997)Google Scholar
  14. 14.
    Shalev-Shwartz, S., Singer, Y.: Online learning: Theory, Algorithms, and Applications (2007)Google Scholar
  15. 15.
    Shumway, R.H., Stoffer, D.S.: Time Series Analysis and its Applications (Springer Texts in Statistics). Springer, Berlin (2005)Google Scholar
  16. 16.
    Yule, G.U.: On a method of investigating periodicities in disturbed series, with special reference to Wolfer’s sunspot numbers. In: Philosophical Transactions of the Royal Society of London. Series A, Containing Papers of a Mathematical or Physical Character, vol. 226, pp. 267–298 (1927)CrossRefGoogle Scholar
  17. 17.
    Zinkevich, M.: Online convex programming and generalized infinitesimal gradient ascent. In: Proceedings of the 20th International Conference on Machine Learning (ICML-03), pp. 928–936 (2003)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.LGIPMUniversity of LorraineMetzFrance

Personalised recommendations