[论文解读] Prediction of Brent crude oil price based on LSTM model under the background of low-carbon transition

Yuwen Zhao, Baojun Hu+1 more|arXiv (Cornell University)|Sep 19, 2024

Hydrocarbon exploration and reservoir analysis被引用 6

一句话总结

论文使用三层LSTM在近端预测布伦特原油价格，利用EIA现货数据，探讨低碳转型因素对价格动态的影响。

ABSTRACT

In the field of global energy and environment, crude oil is an important strategic resource, and its price fluctuation has a far-reaching impact on the global economy, financial market and the process of low-carbon development. In recent years, with the gradual promotion of green energy transformation and low-carbon development in various countries, the dynamics of crude oil market have become more complicated and changeable. The price of crude oil is not only influenced by traditional factors such as supply and demand, geopolitical conflict and production technology, but also faces the challenges of energy policy transformation, carbon emission control and new energy technology development. This diversified driving factor makes the prediction of crude oil price not only very important in economic decision-making and energy planning, but also a key issue in financial markets.In this paper, the spot price data of European Brent crude oil provided by us energy information administration are selected, and a deep learning model with three layers of LSTM units is constructed to predict the crude oil price in the next few days. The results show that the LSTM model performs well in capturing the overall price trend, although there is some deviation during the period of sharp price fluctuation. The research in this paper not only verifies the applicability of LSTM model in energy market forecasting, but also provides data support for policy makers and investors when facing the uncertainty of crude oil price.

研究动机与目标

• 在低碳转型及能源政策变化背景下，推动布伦特原油价格的预测。
• 评估LSTM神经网络在能源市场预测中的适用性。
• 在油价不确定性下，为决策者和投资者提供基于数据的洞见。

提出的方法

• 构建三层LSTM神经网络，预测未来几日布伦特价格。
• 以美国能源信息署（U.S. Energy Information Administration）的欧洲布伦特原油现货价格数据作为输入。
• 评估模型在捕捉总体价格趋势及在剧烈波动期间的偏离的能力。

实验结果

研究问题

• RQ1 三层LSTM是否能准确捕捉布伦特原油价格的总体趋势？
• RQ2 在价格快速波动期，LSTM预测与实际价格的对齐程度如何？
• RQ3 低碳转型相关因素对布伦特价格预测性能有何影响？

主要发现

• LSTM模型能够捕捉布伦特原油价格的总体趋势。
• 在价格剧烈波动期，预测存在一定偏差。
• 该方法展示了LSTM在能源市场预测中的适用性，并为决策者和投资者提供数据支持。