Crude Oil Price Prediction using Slantlet Denoising based Hybrid Models
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.
Kaijian He Kin Keung Lai Jerome Yen
Department of Management Sciences, City University of Hong Kong Tat Chee Avenue, Kowloon, Hong Kong Department of Finance, Hong Kong University of Science and Technology Clear Water Bay, Kowloon, Hong
国际会议
三亚
英文
1062-1066
2009-04-24（万方平台首次上网日期，不代表论文的发表时间）