Forecasting daily oil prices using a multi-modal transformer with sentiment-guided attention

Abstract

This study presents a novel forecasting framework, the Multi-Modal Transformer with Sentiment-Guided Attention (MMT-SGA), designed to enhance daily oil price predictions. Recognizing the limitations of traditional linear and statistical methods in capturing oil price volatility, the proposed model integrates structured numerical data and unstructured textual sentiment analysis through advanced transformer architectures. The sentiment-guided attention mechanism dynamically adjusts predictions based on real-time sentiment volatility, significantly improving forecasting accuracy and responsiveness. Comprehensive numerical experiments conducted over a decade of data (2015–2024) demonstrate the model’s superior performance compared to established methods such as ARIMA, Random Forest, XGBoost, LSTM, and TCN. Results highlight MMT-SGA's robustness, interpretability, and adaptability in complex and volatile market environments, underscoring its potential for informed decision-making in economic and policy contexts.