Year: 2025
Type: article
Abstract: This study presents an advanced deep learning framework for forecasting gold futures prices by integrating a dual-attention Long Short-Term Memory (LSTM) network with a comprehensive set of engineered technical indicators. The proposed model employs temporal attention to dynamically reweight historical time steps and feature-level attention to adaptively prioritize influential indicators such as momentum, volatility, and trend measures. This dual mechanism enables the network to capture nonlinear dependencies and shifting market regimes more effectively than conventional models. Empirical evaluation using COMEX Gold Futures (Ticker: GC1) data obtained from Investing.com, covering the period January 2010 to May 2025, demonstrates the model’s superior forecasting accuracy. The proposed framework achieves a Mean Absolute Percentage Error (MAPE) of 0.91% and a coefficient of determination (R²) of 0.995 after calibration, representing a 34.7% reduction in MAE compared with the baseline LSTM. The lag-aware calibration module further refines short-term directional forecasts. At the same time, the dual-attention layers enhance interpretability by revealing the relative importance of indicators and time intervals across market conditions. By combining sequential modeling, adaptive feature selection, and explainable attention visualization, this research delivers a transparent, scalable, and high-performance forecasting framework. The findings offer practical value for traders, analysts, and policymakers seeking reliable and interpretable tools to navigate uncertainty and volatility in commodity markets.
DOI: 10.14419/kexs8879
Authors: Sa’eed Serwan Abdulsattar, Hani Al-Balasmeh, Rahmeh Abdulkaeem Jaber, Fayzeh Abdulkareem Jaber
Institutions: University of Bahrain, University of Technology Bahrain
Field: Decision Sciences
Domain: Social Sciences
Open Access status: diamond
OpenAlex ID: W4415959002
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