TY - GEN
T1 - Oil price forecasting with an EMD-based multiscale neural network learning paradigm
AU - Yu, Lean
AU - Lai, Kin Keung
AU - Wang, Shouyang
AU - He, Kaijian
PY - 2007
Y1 - 2007
N2 - 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. © Springer-Verlag Berlin Heidelberg 2007.
AB - 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. © Springer-Verlag Berlin Heidelberg 2007.
KW - Artificial neural networks
KW - Crude oil price forecasting
KW - Empirical mode decomposition
KW - Multiscale learning paradigm
UR - http://www.scopus.com/inward/record.url?scp=38149016329&partnerID=8YFLogxK
UR - https://www.scopus.com/record/pubmetrics.uri?eid=2-s2.0-38149016329&origin=recordpage
UR - https://app-overton-io.ezproxy.cityu.edu.hk/articles.php?query=10.1007/978-3-540-72588-6_148
U2 - 10.1007/978-3-540-72588-6_148
DO - 10.1007/978-3-540-72588-6_148
M3 - RGC 32 - Refereed conference paper (with host publication)
SN - 9783540725879
VL - 4489 LNCS
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 925
EP - 932
BT - Computational Science - ICCS 2007
PB - Springer Verlag
T2 - 7th International Conference on Computational Science (ICCS 2007)
Y2 - 27 May 2007 through 30 May 2007
ER -