A deep learning ensemble approach for crude oil price forecasting

By Yang Zhao, Jianping Li, Lean Yu. 2017

348 citations recorded by the source corpus.

Subfields: Economics and Econometrics, Electrical and Electronic Engineering, Management Science and Operations Research, Time-Series & Forecasting

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Economics and Econometrics

1. Omer Berat Sezer
2. Ruijin Du
3. Minggang Wang
4. H. Eugene Stanley
5. Lin Chen
6. Chao Wang
7. Longfeng Zhao
8. A. Ozbayoglu

In Electrical and Electronic Engineering

1. A. Ozbayoglu
2. M. Ugur Gudelek
3. Omer Berat Sezer
4. Wei Yu
5. William Grant Hatcher
6. Longbing Cao
7. Weiwei Jiang

In Management Science and Operations Research

1. M. Ugur Gudelek
2. Omer Berat Sezer
3. A. Ozbayoglu
4. Wei Yu
5. H. Eugene Stanley
6. William Grant Hatcher
7. Weiwei Jiang
8. Longbing Cao

In Time-Series & Forecasting

1. M. Ugur Gudelek
2. Omer Berat Sezer
3. A. Ozbayoglu
4. Wei Yu
5. William Grant Hatcher
6. Weiwei Jiang
7. Longbing Cao

Citations in the corpus

• Financial Time Series Forecasting with Deep Learning : A Systematic Literature Review: 2005-2019 (2019, methodology): “In [157], FNN and Stacked Denoising Autoencoders (SDAE) deep models were compared against Support Vector Regressor (SVR), Random Walk (RW) and Markov Regime Switching (MRS) models for WTI oil price forecasting.”
• A novel hybrid method of forecasting crude oil prices using complex network science and artificial intelligence algorithms (2018, methodology): “The Diebold-Mariano (DM) statistic [24,56] is used to measure the di ff erences in the predictive accuracies of the forecasting models.”
• Deep Learning Sensor Fusion for Autonomous Vehicle Perception and Localization: A Review (2020, neutral): “[64,67] ] Uncertainty in Recently, deep learning is being extensively used in many di ff erent applications, such as in object detection [77], environment segmentation, semantic object identification, healthcare [78], self-driving vehicles [79,80], and finance [81], to name a few.”
• A Survey of Deep Learning: Platforms, Applications and Emerging Research Trends (2018, positive): “[166] introduced a deep learning ensemble approach for crude oil price forecasting using stacked deep autoencoders trained on bootstrap aggregation or bagging.”
• A Survey on ensemble learning under the era of deep learning (2021, background): “DA6 Crude oil price forecasting (Zhao et al. 2017) 162 2017”
• Analyzing Crude Oil Prices under the Impact of COVID-19 by Using LSTARGARCHLSTM (2020, neutral): “[58] used LSTM for oil price forecasting.”