Seeing is believing: Forecasting crude oil price trend from the perspective of images

Xiaohang Ren, Wenting Jiang, Qiang Ji and Pengxiang Zhai

Journal of Forecasting, 2024, vol. 43, issue 7, 2809-2821

Abstract: 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.

Date: 2024
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https://doi.org/10.1002/for.3149

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Persistent link: https://EconPapers.repec.org/RePEc:wly:jforec:v:43:y:2024:i:7:p:2809-2821

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