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...

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Vydáno v:	Energy economics Ročník 66; s. 9 - 16
Hlavní autoři:	Zhao, Yang, Li, Jianping, Yu, Lean
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	Kidlington Elsevier B.V 01.08.2017 Elsevier Science Ltd
Témata:	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
On-line přístup:	Získat plný text
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