Title :
A Comparative Study of Multi-step-ahead Prediction for Crude Oil Price with Support Vector Regression
Author :
Bao, Yukun ; Yang, Yunfei ; Xiong, Tao ; Zhang, Jinlong
Author_Institution :
Dept. of Manage. Sci. & Inf. Syst., Huazhong Univ. of Sci.&Tech., Wuhan, China
Abstract :
Accurate prediction on crude oil price in a long time horizon has been appealing both for academia and practitioners. Recursive strategy and direct strategy are two mainstream modeling schemas widely used for multi-step-ahead prediction in the context of time series modeling. In this paper, a comparative study has been conducted to justify these two strategies in multi-step-ahead prediction for crude oil price with Support Vector Regression (SVR). The experimental results show the direct strategy has more consistent performance than recursive one in the various experimental setting.
Keywords :
crude oil; forecasting theory; regression analysis; support vector machines; time series; crude oil price; long time horizon; multistep-ahead prediction; support vector regression; time series modeling; Artificial neural networks; Computational modeling; Forecasting; Kernel; Predictive models; Support vector machines; Time series analysis; Crude Oil Price Predicition; Multip-step-aheand Prediction; Support Vector Regression; Time Sereis Modeling;
Conference_Titel :
Computational Sciences and Optimization (CSO), 2011 Fourth International Joint Conference on
Conference_Location :
Yunnan
Print_ISBN :
978-1-4244-9712-6
Electronic_ISBN :
978-0-7695-4335-2
DOI :
10.1109/CSO.2011.70
