Crude Oil Price Forecast Based on Deep Transfer Learning: Shanghai Crude Oil as an Example

Chao Deng, Liang Ma and Taishan Zeng
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Chao Deng: Business School, Central South University, Changsha 410083, China
Liang Ma: Business School, Central South University, Changsha 410083, China
Taishan Zeng: School of Mathematics, South China Normal University, Guangzhou 510631, China

Sustainability, 2021, vol. 13, issue 24, 1-13

Abstract: Crude oil is an important fuel resource for all countries. Accurate predictions of oil prices have important economic and social values. However, the price of crude oil is highly nonlinear under the influence of many factors, so it is very difficult to predict accurately. Shanghai crude oil futures were officially listed in March 2018. It is of great significance to accurately predict the price of Shanghai crude oil futures for guiding China’s domestic production practice. Forecasting the price of Shanghai crude oil futures is even more difficult because of the lack of price data due to the short listing time. In order to solve this problem, this paper proposes using Long Short-Term Memory Network (LSTM) based on transfer learning to predict the price of crude oil in Shanghai. The basic idea is to take advantage of the correlation between Brent crude oil and Shanghai crude oil, use Brent crude oil for training in the early stage, and then use Shanghai crude oil to fine-tune the network. The empirical results show that the LSTM model based on transfer learning has strong generalization ability and high prediction accuracy.

Keywords: Shanghai crude oil; deep learning; time-series; LSTM network; transfer learning (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

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