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An Improved EMD Online Learning-Based Model for Gold Market Forecasting

  • Shifei Zhou
  • Kin Keung Lai
Part of the Smart Innovation, Systems and Technologies book series (SIST, volume 10)

Abstract

In this paper, an improved EMD (Empirical Mode Decomposition) online learning-based model for gold market forecasting is proposed. First, we adopt the EMD method to divide the time series data into different subsets. Second, a back-propagation neural network model (BPNN) is used to function as the prediction model in our system. We update the online learning rate of BPNN instantly as well as the weight matrix. Finally, a rating method is used to identify the most suitable BPNN model for further prediction. The experiment results show that our system has a good forecasting performance.

Keywords

Root Mean Square Error Weight Matrix Learning Rate Online Learning Empirical Mode Decomposition 
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 2011

Authors and Affiliations

  • Shifei Zhou
    • 1
  • Kin Keung Lai
    • 2
  1. 1.Department of Management Sciences, Collage of BusinessCity University of Hong KongKowloonHong Kong
  2. 2.School of business and managementNorth China Electric Power UniversityBeijingChina

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