A deep learning ensemble approach for crude oil price forecasting

As crude oil price is influenced by numerous factors, capturing its behavior precisely is quite challenging, and thus leads to the difficulty of forecasting. In this study, a deep learning ensemble approach is proposed to deal with this problem. In our approach, two techniques are utilized. One is a...

Full description

Saved in:

Bibliographic Details
Published in:	Energy economics Vol. 66; pp. 9 - 16
Main Authors:	Zhao, Yang, Li, Jianping, Yu, Lean
Format:	Journal Article
Language:	English
Published:	Kidlington Elsevier B.V 01.08.2017 Elsevier Science Ltd
Subjects:	Artificial neural networks Bagging Cognitive style Crude oil Crude oil price forecasting Crude oil prices Data analysis Datasets Deep learning Denoising Energy economics Ensemble learning Forecasting Mathematical models Multivariate forecasting Networks Neural networks Noise reduction Petroleum Stacked denoising autoencoder Studies Training C53 Deep learning C52 C63 Crude oil price forecasting C45 Q47 Stacked denoising autoencoder E37 Ensemble learning Bagging Multivariate forecasting
ISSN:	0140-9883, 1873-6181
Online Access:	Get full text
Tags:	Add Tag No Tags, Be the first to tag this record!

Be the first to leave a comment!