Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
6901311 | Procedia Computer Science | 2017 | 8 Pages |
Abstract
With the popularity of the deep learning model in the engineering fields, it has attracted significant research interests in the economic and finance fields. In this paper, we use the deep learning model to capture the unknown complex nonlinear characteristics of the crude oil price movement. We further propose a new hybrid crude oil price forecasting model based on the deep learning model. Using the proposed model, major crude oil price movement is analyzed and modeled. The performance of the proposed model is evaluated using the price data in the WTI crude oil markets. The empirical results show that the proposed model achieves the improved forecasting accuracy.
Related Topics
Physical Sciences and Engineering
Computer Science
Computer Science (General)
Authors
Yanhui Chen, Kaijian He, Geoffrey K.F. Tso,