An Explainable CNN–LSTM Framework for Monthly Crude Oil Price Forecasting Using WTI Time Series Data

Thongjamroon, Joompol
Phimphisan, Songgrod
Sriwiboon, Nattavut

Journal of Robotics and Control (JRC)
Vol. 6 No. 5 (2025)

Crude oil price forecasting has posed significant challenges due to its volatility and nonlinear dynamics. This study has proposed an explainable CNN–LSTM framework to predict monthly West Texas Intermediate (WTI) crude oil prices. The model has captured both local and sequential patterns without using external inputs or decomposition. Trained over 50 epochs across three data splits, it has been evaluated using RMSE, MAE, MASE, SMAPE, and directional accuracy. A classification accuracy of 92.4% and directional accuracy of up to 87.4% have been achieved. The model has consistently outperformed classical and hybrid baselines, with statistical significance confirmed by the Friedman–Nemenyi test. Saliency-based interpretability has further enhanced transparency, making the framework suitable for real-world energy forecasting.