Forecasting Crude Oil Prices Using a Convolutional Neural Network with Time-Delay Embedding

Kaijian He, Lean Yu, Jia Liu and Yingchao Zou
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Kaijian He: College of Tourism, Hunan Normal University, Changsha 410081, P. R. China
Lean Yu: Business School, Sichuan University, Chengdu 610065, P. R. China
Jia Liu: School of Accounting, Economics and Finance, University of Portsmouth, England PO1 3DE, UK
Yingchao Zou: College of Tourism, Hunan Normal University, Changsha 410081, P. R. China

International Journal of Information Technology & Decision Making (IJITDM), 2025, vol. 24, issue 03, 843-863

Abstract: This paper proposes a novel hybrid forecasting model, TDE-CNN, to model the complex dynamics of crude oil price movements. The model integrates Time-Delay Embedding (TDE) Method with a Convolutional Neural Network (CNN) to leverage both spatial and temporal information. The TDE-CNN model uses the TDE method to transform raw crude oil data into higher-dimensional space to reveal underlying spatio-temporal patterns, while the CNN effectively models these patterns for improved predictive accuracy. The TDE-CNN model is applied to forecast major crude oil spot price movements, and its forecasting performance has been comprehensively and rigorously evaluated. Empirical results demonstrate that the TDE-CNN model achieves lower forecasting errors compared to benchmark models, as measured by Mean Squared Error (MSE). Additionally, the Diebold-Mariano test confirms that the improvement in forecasting accuracy is statistically significant.

Keywords: Spatio-temporal data feature; data-characteristic-driven forecasting methodology; mixing data characteristic; convolutional neural network; time delayed embedding; crude oil price forecasting (search for similar items in EconPapers)
Date: 2025
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DOI: 10.1142/S0219622025410020

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