Forecasting Commodity Price Shocks Using Temporal and Semantic Fusion of Prices Signals and Agentic Generative AI Extracted Economic News

ArXiv ID: 2508.06497 “View on arXiv”

Authors: Mohammed-Khalil Ghali, Cecil Pang, Oscar Molina, Carlos Gershenson-Garcia, Daehan Won

Abstract

Accurate forecasting of commodity price spikes is vital for countries with limited economic buffers, where sudden increases can strain national budgets, disrupt import-reliant sectors, and undermine food and energy security. This paper introduces a hybrid forecasting framework that combines historical commodity price data with semantic signals derived from global economic news, using an agentic generative AI pipeline. The architecture integrates dual-stream Long Short-Term Memory (LSTM) networks with attention mechanisms to fuse structured time-series inputs with semantically embedded, fact-checked news summaries collected from 1960 to 2023. The model is evaluated on a 64-year dataset comprising normalized commodity price series and temporally aligned news embeddings. Results show that the proposed approach achieves a mean AUC of 0.94 and an overall accuracy of 0.91 substantially outperforming traditional baselines such as logistic regression (AUC = 0.34), random forest (AUC = 0.57), and support vector machines (AUC = 0.47). Additional ablation studies reveal that the removal of attention or dimensionality reduction leads to moderate declines in performance, while eliminating the news component causes a steep drop in AUC to 0.46, underscoring the critical value of incorporating real-world context through unstructured text. These findings demonstrate that integrating agentic generative AI with deep learning can meaningfully improve early detection of commodity price shocks, offering a practical tool for economic planning and risk mitigation in volatile market environments while saving the very high costs of operating a full generative AI agents pipeline.

Keywords: Agentic Generative AI, Dual-stream LSTM, Semantic News Embedding, Commodity Price Spike Forecasting, Attention Mechanisms, Commodities

Complexity vs Empirical Score

  • Math Complexity: 6.5/10
  • Empirical Rigor: 8.5/10
  • Quadrant: Holy Grail
  • Why: The paper employs advanced deep learning architectures (dual-stream LSTM with attention, news embeddings) and statistical evaluation (AUC, ablation studies), while demonstrating substantial empirical rigor through a 64-year dataset, real-world data integration, and direct comparisons against established baselines.
  flowchart TD
    A["Research Goal<br>Forecast Commodity Price Shocks"] --> B["Data & Inputs<br>Prices (1960-2023) & Economic News"]
    B --> C["Methodology<br>Agentic GenAI for News Extraction"]
    C --> D["Computational Process<br>Dual-Stream LSTM + Attention Fusion"]
    D --> E["Key Outcomes<br>High Accuracy (0.91) & AUC (0.94)"]
    E --> F["Insight<br>News Context is Critical for Performance"]