Document Type


Publication Date




Publication Title

Journal of Risk and Financial Management






503 (1-22)


Crude oil is one of the world’s most important commodities. Its price can affect the global economy, as well as the economies of importing and exporting countries. As a result, forecasting the price of crude oil is essential for investors. However, crude oil price tends to fluctuate considerably during significant world events, such as the COVID-19 pandemic and geopolitical conflicts. In this paper, we propose a deep learning model for forecasting the crude oil price of one-step and multi-step ahead. The model extracts important features that impact crude oil prices and uses them to predict future prices. The prediction model combines convolutional neural networks (CNN) with long short-term memory networks (LSTM). We compared our one-step CNN–LSTM model with other LSTM models, the CNN model, support vector machine (SVM), and the autoregressive integrated moving average (ARIMA) model. Also, we compared our multi-step CNN–LSTM model with LSTM, CNN, and the time series encoder–decoder model. Extensive experiments were conducted using short-, medium-, and long-term price data of one, five, and ten years, respectively. In terms of accuracy, the proposed model outperformed existing models in both one-step and multi-step predictions.


© 2023 by the authors.

This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution 4.0 International (CC BY 4.0) License.

Data Availability

Article states: "Data can be downloaded from MarketWatch (, accessed on 31 August 2023).

Original Publication Citation

Aldabagh, H., Zheng, X., & Mukkamala, R. (2023). A hybrid deep learning approach for crude oil price prediction. Journal of Risk and Financial Management, 16(12), 1-22, Article 503.


0000-0003-2695-9642 (Zheng), 0000-0001-6323-9789 (Mukkamala)