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Hybridizing Exponential Smoothing and Neural Network for Financial Time Series Predication

  • Kin Keung Lai
  • Lean Yu
  • Shouyang Wang
  • Wei Huang
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3994)

Abstract

In this study, a hybrid synergy model integrating exponential smoothing and neural network is proposed for financial time series prediction. The proposed model attempts to incorporate the linear characteristics of an exponential smoothing model and nonlinear patterns of neural network to create a “synergetic” model via the linear programming technique. For verification, two real-world financial time series are used for testing purpose.

Keywords

Root Mean Square Error Artificial Neural Network Model Exponential Smoothing Time Series Forecast Financial Time Series 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Kin Keung Lai
    • 1
    • 2
  • Lean Yu
    • 2
    • 3
  • Shouyang Wang
    • 1
    • 3
  • Wei Huang
    • 4
  1. 1.College of Business AdministrationHunan UniversityChangshaChina
  2. 2.Department of Management SciencesCity University of Hong KongKowloon, Hong Kong
  3. 3.Institute of Systems ScienceAcademy of Mathematics and Systems Science, Chinese Academy of SciencesBeijingChina
  4. 4.School of ManagementHuazhong University of Science and TechnologyWuhanChina

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