Attention-Enhanced LSTM Deep Learning Network for Gold ‎Futures Forecasting - Abdulsattar , Sa’eed Serwan; Al-Balasmeh, Hani; Jaber, Rahmeh Abdulkaeem; Jaber, Fayzeh Abdulkareem (International Journal of Basic and Applied Sciences, 2025)
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.
Keywords: Gold Futures; Deep Learning; LSTM; Attention Mechanism; Time Series Forecasting