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February 8, 2026Open Access

Attention-Based Deep Learning Hybrid Model for Cash Crop Price Forecasting: Evidence from Global Futures Markets with Implications for West Africa

Key Points

This research aims to improve agricultural commodity price forecasting using a hybrid deep learning model.
Proposed a hybrid long short-term memory-multi-head attention (LSTM-MHA) framework.
Evaluated model performance on multivariate global commodity futures prices.
Conducted an ablation study to analyze forecasting accuracy versus interpretability.
Achieved a mean squared error (MSE) of 0.0124, root mean squared error (RMSE) of 0.1114, and mean absolute error (MAE) of 0.1097.
Outperformed conventional models like ARIMA and standalone LSTM by three to four times in error reduction.
Demonstrated effective capturing of short-term temporal dependencies in price dynamics.

Abstract

Accurate forecasting of agricultural commodity prices is essential for managing market volatility, improving supply chain coordination, and supporting food security-related decision-making. Recent advances in deep learning have demonstrated strong potential for capturing nonlinear and temporal dependencies in commodity price dynamics. In this study, we propose a hybrid long short-term memory–multi-head attention (LSTM–MHA) framework for agricultural commodity price forecasting using global futures market data. The model is trained and evaluated on multivariate global commodity futures prices, reflecting internationally traded benchmark markets rather than region-specific domestic prices. While the empirical analysis is based on global data, the study is motivated by the relevance of international price movements for import-dependent regions, particularly West Africa, where global price transmission plays a critical role in domestic market dynamics. The experimental results demonstrate that the proposed model effectively captures short-term temporal dependencies and provides interpretable attention-based insights into lag relevance. An ablation study further highlights the trade-offs between forecasting accuracy and interpretability across different model configurations. The hybrid architecture combines the time-based pattern identification and weighting capabilities of multi-head attention with the sequential learning capabilities of LSTM. Mean absolute error (MAE), root mean squared error (RMSE), and mean squared error (MSE) were used to evaluate the model’s performance. With an MSE of 0.0124, an RMSE of 0.1114, and an MAE of 0.1097, the model outperformed conventional models like ARIMA and standalone LSTM by three to four times in error reduction. The findings suggest that attention-enhanced deep learning models can serve as valuable analytical tools for understanding global price dynamics and informing policy analysis and risk management in West African agricultural markets.

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