Oil Price Forecasting with an EMD-Based Multiscale Neural Network Learning Paradigm

In this study, a multiscale neural network learning paradigm based on empirical mode decomposition (EMD) is proposed for crude oil price prediction. In this learning paradigm, the original price series are first decomposed into various independent intrinsic mode components (IMCs) with a range of frequency scales. Then the internal correlation structures of different IMCs are explored by neural network model. With the neural network weights, some important IMCs are selected as final neural network inputs and some unimportant IMCs that are of little use in the mapping of input to output are discarded. Finally, the selected IMCs are input into another neural network model for prediction purpose. For verification, the proposed multiscale neural network learning paradigm is applied to a typical crude oil price -- West Texas Intermediate (WTI) crude oil spot price prediction.

[1]  N. Huang,et al.  A new view of nonlinear water waves: the Hilbert spectrum , 1999 .

[2]  Kin Keung Lai,et al.  CRUDE OIL PRICE FORECASTING WITH TEI@I METHODOLOGY ∗ , 2005 .

[3]  Yao Liang,et al.  Multiresolution learning paradigm and signal prediction , 1997, IEEE Trans. Signal Process..

[4]  Anders Krogh,et al.  Neural Network Ensembles, Cross Validation, and Active Learning , 1994, NIPS.

[5]  Kurt Hornik,et al.  Multilayer feedforward networks are universal approximators , 1989, Neural Networks.

[6]  Yong Shi,et al.  Data Mining and Knowledge Management , 2008 .

[7]  Xiaoli Li.  Temporal structure of neuronal population oscillations with empirical model decomposition , 2006 .

[8]  Kin Keung Lai,et al.  A Novel Hybrid AI System Framework for Crude Oil Price Forecasting , 2004, CASDMKM.

[9]  Hillard G. Huntington,et al.  Oil Price Forecasting in the 1980s: What Went Wrong?* , 1994 .

[10]  Claudio Morana,et al.  A semiparametric approach to short-term oil price forecasting , 2001 .

[11]  Bruce Abramson,et al.  Probabilistic forecasts from probabilistic models: A case study in the oil market , 1995 .

[12]  Andrzej Cichocki,et al.  Neural networks for optimization and signal processing , 1993 .

[13]  Kin Keung Lai,et al.  A novel nonlinear ensemble forecasting model incorporating GLAR and ANN for foreign exchange rates , 2005, Comput. Oper. Res..

[14]  Andreas S. Weigend,et al.  Time Series Prediction: Forecasting the Future and Understanding the Past , 1994 .

[15]  N. Huang,et al.  The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis , 1998, Proceedings of the Royal Society of London. Series A: Mathematical, Physical and Engineering Sciences.