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.
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Zhou, S., Lai, K.K. (2011). An Improved EMD Online Learning-Based Model for Gold Market Forecasting. In: Watada, J., Phillips-Wren, G., Jain, L.C., Howlett, R.J. (eds) Intelligent Decision Technologies. Smart Innovation, Systems and Technologies, vol 10. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-22194-1_8
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DOI: https://doi.org/10.1007/978-3-642-22194-1_8
Publisher Name: Springer, Berlin, Heidelberg
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