Forecasting Crude Oil Prices: a Deep Learning based Model

By Yanhui Chen, Kaijian He, Geoffrey Kwok Fai Tso. 2017

110 citations recorded by the source corpus.

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.

Subfields: Time-Series & Forecasting

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In Time-Series & Forecasting

1. M. Ugur Gudelek
2. Omer Berat Sezer
3. A. Ozbayoglu
4. Weiwei Jiang
5. Longbing Cao

Citations in the corpus

• Financial Time Series Forecasting with Deep Learning : A Systematic Literature Review: 2005-2019 (2019, methodology): “[158] WTI Crude Oil Prices 2007-2017 Price data ARMA + DBN, RW + LSTM MSE Python, Keras, Tensorflow”
• Analyzing Crude Oil Prices under the Impact of COVID-19 by Using LSTARGARCHLSTM (2020, background): “[59] suggested the deep learning model to forecast oil prices.”