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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)

Abstract

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.

Keywords

Hybrid EMD-ANN model EMD ANN SVR Nifty Time series 

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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

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