TY - GEN
T1 - Crude oil price prediction using slantlet denoising based hybrid models
AU - He, Kaijian
AU - Lai, Kin Keung
AU - Yen, Jerome
PY - 2009
Y1 - 2009
N2 - The accurate prediction of crude oil price movement has always been the central issue with profound implications across different levels of the economy. This study conducts empirical investigations into the characteristics of crude oil market and proposes a novel Slantlet denoising based hybrid methodology for the prediction of its movement. The proposed algorithm models the underlying data characteristics in a more refined manner, integrating linear models such as ARMA and nonlinear models such as Support Vector Regression. Empirical studies confirm the superiority of the proposed Slantlet based hybrid models against benchmark alternatives. The performance improvement is attributed to the finer separation of complicated factors influencing the crude oil behaviors into linear and nonlinear components in the multi scale domain, which improves the goodness of fit and reduces the overfitting issue. © 2009 IEEE.
AB - The accurate prediction of crude oil price movement has always been the central issue with profound implications across different levels of the economy. This study conducts empirical investigations into the characteristics of crude oil market and proposes a novel Slantlet denoising based hybrid methodology for the prediction of its movement. The proposed algorithm models the underlying data characteristics in a more refined manner, integrating linear models such as ARMA and nonlinear models such as Support Vector Regression. Empirical studies confirm the superiority of the proposed Slantlet based hybrid models against benchmark alternatives. The performance improvement is attributed to the finer separation of complicated factors influencing the crude oil behaviors into linear and nonlinear components in the multi scale domain, which improves the goodness of fit and reduces the overfitting issue. © 2009 IEEE.
UR - http://www.scopus.com/inward/record.url?scp=70449347604&partnerID=8YFLogxK
UR - https://www.scopus.com/record/pubmetrics.uri?eid=2-s2.0-70449347604&origin=recordpage
U2 - 10.1109/CSO.2009.449
DO - 10.1109/CSO.2009.449
M3 - RGC 32 - Refereed conference paper (with host publication)
SN - 9780769536057
VL - 2
SP - 12
EP - 16
BT - Proceedings of the 2009 International Joint Conference on Computational Sciences and Optimization, CSO 2009
T2 - 2009 International Joint Conference on Computational Sciences and Optimization, CSO 2009
Y2 - 24 April 2009 through 26 April 2009
ER -