A Hybrid EMD-ANN Model for Stock Price Prediction

  • Dhanya JothimaniEmail author
  • Ravi Shankar
  • Surendra S. Yadav
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9873)


Financial time series such as foreign exchange rate and stock index, in general, exhibit non-linear and non-stationary behavior. Statistical models and machine learning models, often, fail to predict time series with such behavior. Former models are prone to large statistical errors. While machine learning models such as Support Vector Machines (SVM) and Artificial Neural Network (ANN) suffer from the limitations of overfitting and getting stuck in local minima, etc. In this paper, a hybrid model integrating the advantages of Empirical Mode Decomposition (EMD) and ANN is used to predict the short-term forecasts of Nifty stock index. In first stage, EMD is used to decompose the time series into a set of subseries, namely, intrinsic mode function (IMF) and residue component. In the next stage, ANN is used to predict each IMF independently along with residue component. The results show that the hybrid EMD-ANN model outperformed both SVR and ANN models without decomposition.


Hybrid EMD-ANN model EMD ANN SVR Nifty Time series 


  1. 1.
    Atsalakis, G., Valavanis, K.: Surveying stock market forecasting techniques- Part II: soft computing methods. Expert Syst. Appl. 36(3, Part 2), 5932–5941 (2009). CrossRefGoogle Scholar
  2. 2.
    Atsalakis, G., Valavanis, K.: Surveying stock market forecasting techniques- Part I: conventional methods. In: Zopounidis, C. (ed.) Computation Optimization in Economics and Finance Research Compendium, pp. 49–104. Nova Science Publishers Inc., New York (2013)Google Scholar
  3. 3.
    Box, G.E.P., Jenkins, G.: Time Series Analysis, Forecasting and Control. Holden-Day, Incorporated, San Francisco (1990)zbMATHGoogle Scholar
  4. 4.
    Cadenas, E., Rivera, W.: Wind speed forecasting in three different regions of Mexico, using a hybrid ARIMA-ANN model. Renew. Energy 35(12), 2732–2738 (2010). CrossRefGoogle Scholar
  5. 5.
    Crone, S., Guajardo, J., Weber, R.: The impact of preprocessing on support vector regression and neural networks in time series prediction. In: Proceedings of the International Conference on Data Mining (DMIN 2006), pp. 37–42. CSREA, Las Vegas (2006)Google Scholar
  6. 6.
    Crowley, P.: Long cycles in growth: explorations using new frequency domain techniques with US data. Bank of Finland Research Discussion Paper No. 6/2010, February 2010Google Scholar
  7. 7.
    Dickey, D.A., Fuller, W.A.: Distribution of the estimators for autoregressive time series with a unit root. J. Am. Stat. Assoc. 74(366), 427–431 (1979). MathSciNetCrossRefzbMATHGoogle Scholar
  8. 8.
    Dickey, D.A., Fuller, W.A.: Likelihood ratio statistics for autoregressive time series with a unit root. Econometrica 49(4), 1057–1072 (1981). MathSciNetCrossRefzbMATHGoogle Scholar
  9. 9.
    Diebold, F.X., Mariano, R.S.: Comparing predictive accuracy. J. Bus. Econ. Stat. 13, 253–265 (1995)Google Scholar
  10. 10.
    Huang, N., Shen, Z., Long, S., Wu, M., Shih, H., Zheng, Q., Yen, N., Tung, C., Liu, H.: The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis. Proc. R. Soc. Lond. A Math. Phys. Eng. Sci. 454(1971), 903–995 (1998)MathSciNetCrossRefzbMATHGoogle Scholar
  11. 11.
    Jothimani, D., Shankar, R., Yadav, S.S.: Discrete wavelet transform-based prediction of stock index: a study on National Stock Exchange Fifty index. J. Financ. Manage. Anal. 28(2), 35–49 (2015)Google Scholar
  12. 12.
    Jothimani, D., Shankar, R., Yadav, S.S.: A comparative study of ensemble-based forecasting models for stock index prediction. In: Proceedings of MWAIS 2016, paper 5 (2016).
  13. 13.
    Kao, L.J., Chiu, C.C., Lu, C.J., Chang, C.H.: A hybrid approach by integrating wavelet-based feature extraction with MARS and SVR for stock index forecasting. Decis. Support Syst. 54(3), 1228–1244 (2013). CrossRefGoogle Scholar
  14. 14.
    Lahmiri, S.: Wavelet low- and high-frequency components as features for predicting stock prices with backpropagation neural networks. J. King Saud Univ. Comput. Inf. Sci. 26(2), 218–227 (2014). Google Scholar
  15. 15.
    Liu, H., Chen, C., Tian, H., Li, Y.: A hybrid model for wind speed prediction using empirical mode decomposition and artificial neural networks. Renew. Energy 48, 545–556 (2012). CrossRefGoogle Scholar
  16. 16.
    Matei, M.: Assessing volatility forecasting models: why GARCH models take the lead. J. Econ. Forecast. 4, 42–65 (2009)Google Scholar
  17. 17.
    Murtagh, F., Starck, J., Renaud, O.: On neuro-wavelet modeling. Decis. Support Syst. 37(4), 475–484 (2004). datamining for financial decision makingCrossRefGoogle Scholar
  18. 18.
    Pankratz, A.: Introduction to Box - Jenkins Analysis of a Single Data Series, pp. 24–44. Wiley, Hoboken (2008). Google Scholar
  19. 19.
    Ren, Y., Suganthan, P., Srikanth, N.: A comparative study of empirical mode decomposition-based short-term wind speed forecasting methods. IEEE Trans. Sustain. Ener. 6(1), 236–244 (2015)CrossRefGoogle Scholar
  20. 20.
    Riedmiller, M., Braun, H.: A direct adaptive method for faster backpropagation learning: the RPROP algorithm. In: 1993 IEEE International Conference on Neural Networks, vol. 1, pp. 586–591 (1993)Google Scholar
  21. 21.
    Theodosiou, M.: Forecasting monthly and quarterly time series using STL decomposition. Int. J. Forecast. 27(4), 1178–1195 (2011). CrossRefGoogle Scholar
  22. 22.
    Wu, G., Lo, S.: Effects of data normalization and inherent-factor on decision of optimal coagulant dosage in water treatment by artificial neural network. Expert Syst. Appl. 37(7), 4974–4983 (2010). CrossRefGoogle Scholar
  23. 23.
    Zhang, G.: Time series forecasting using a hybrid ARIMA and neural network model. Neurocomputing 50, 159–175 (2003). CrossRefzbMATHGoogle Scholar

Copyright information

© Springer International Publishing AG 2016

Authors and Affiliations

  • Dhanya Jothimani
    • 1
    Email author
  • Ravi Shankar
    • 1
  • Surendra S. Yadav
    • 1
  1. 1.Department of Management StudiesIndian Institute of Technology DelhiNew DelhiIndia

Personalised recommendations