眼见为实:从图像角度预测原油价格走势

IF 3.4 3区 经济学 Q1 ECONOMICS Journal of Forecasting Pub Date : 2024-05-19 DOI:10.1002/for.3149
Xiaohang Ren, Wenting Jiang, Qiang Ji, Pengxiang Zhai
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引用次数: 0

摘要

在本文中,我们提出了一种新颖的成像方法来预测西德克萨斯中质原油(WTI)期货的每日价格数据。我们使用卷积神经网络(CNN)进行未来价格趋势预测,并获得了比其他基准预测方法更高的预测精度。结果表明,图像可以包含更多非线性信息,这有利于能源价格预测。非线性因素在原油价格剧烈波动时也有很大影响。在鲁棒性测试中,我们发现基于图像的 CNN 是最稳定的方法,可以应用于各种期货预测场景。在低频模型对高频数据的预测中,CNN 方法仍然保持了相当高的预测能力,这表明我们的新方法具有迁移学习的可能性。通过释放图片的力量,我们为预测未来能源趋势开辟了一个全新的视角。
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Seeing is believing: Forecasting crude oil price trend from the perspective of images

In this paper, we propose a novel imaging method to forecast the daily price data of West Texas Intermediate (WTI) crude oil futures. We use convolutional neural networks (CNNs) for future price trend prediction and obtain higher prediction accuracy than other benchmark forecasting methods. The results show that images can contain more nonlinear information, which is beneficial for energy price forecasting. Nonlinear factors also have a strong influence during drastic fluctuations in crude oil prices. In the robustness tests, we find that the image-based CNN is the most stable approach and can be applied in various futures forecasting scenarios. In the prediction of low-frequency models for high-frequency data, the CNN method still retains considerable predictive power, indicating the possibility of transfer learning of our novel approach. By unleashing the power of the picture, we open up a whole new perspective for forecasting future energy trends.

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来源期刊
CiteScore
5.40
自引率
5.90%
发文量
91
期刊介绍: The Journal of Forecasting is an international journal that publishes refereed papers on forecasting. It is multidisciplinary, welcoming papers dealing with any aspect of forecasting: theoretical, practical, computational and methodological. A broad interpretation of the topic is taken with approaches from various subject areas, such as statistics, economics, psychology, systems engineering and social sciences, all encouraged. Furthermore, the Journal welcomes a wide diversity of applications in such fields as business, government, technology and the environment. Of particular interest are papers dealing with modelling issues and the relationship of forecasting systems to decision-making processes.
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