Multilayer feedforward networks are universal approximators
Neural Networks
Neural Networks for Optimization and Signal Processing
Neural Networks for Optimization and Signal Processing
A novel nonlinear ensemble forecasting model incorporating GLAR and ANN for foreign exchange rates
Computers and Operations Research
A novel hybrid AI system framework for crude oil price forecasting
CASDMKM'04 Proceedings of the 2004 Chinese academy of sciences conference on Data Mining and Knowledge Management
Multiresolution learning paradigm and signal prediction
IEEE Transactions on Signal Processing
Computers and Industrial Engineering
A dynamic meta-learning rate-based model for gold market forecasting
Expert Systems with Applications: An International Journal
| Hi-index | 0.00 |
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.