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Farahani Z, Keyghobadi A, Madanchi Zaj M, Hashemi Daran H. A Multi-Source Deep Learning Framework with Attention and SHAP Explainability for Daily Gold Price Forecasting. jor 2026; 23 (3)
URL: http://jamlu.lahijan.iau.ir/article-1-2324-en.html
URL: http://jamlu.lahijan.iau.ir/article-1-2324-en.html
Department of Industrial Management, CT.C., Islamic Azad University, Tehran, Iran , ar.keyghobadi@iau.ac.ir
Abstract: (56 Views)
Gold price forecasting is a challenging task due to the high volatility, nonlinear dynamics, and simultaneous influence of economic and behavioral factors in the XAU/USD market. This paper proposes a multi source deep learning framework with explainability that integrates Gated Recurrent Units (GRU), an attention mechanism, and SHapley Additive exPlanations (SHAP) within a dual stream architecture. In the proposed model, structured market data (OHLC prices and SHAP selected technical indicators) are processed in one stream, while one day lagged sentiment scores extracted from financial news are modeled in a parallel attention based stream. Four experimental scenarios are designed to systematically combine OHLC, technical indicators, and lagged sentiment signals. Empirical results show that the dual stream configuration with OHLC and lagged sentiment achieves the best performance, with a root mean squared error (RMSE) of 27.75 and a mean absolute error (MAE) of 19.73, outperforming single stream GRU baselines and classical time series models across RMSE, MAE, MAPE, and R². SHAP based feature selection not only reduces input dimensionality but also enhances interpretability by highlighting the dominant role of short term exponential moving averages and momentum indicators. Overall, the findings indicate that intelligent, multi source, and explainable architectures can provide more accurate and reliable forecasts for volatile financial markets such as gold.
Type of Study: Research |
Subject:
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Received: 2026/01/3 | Accepted: 2026/05/13 | Published: 2026/09/11
Received: 2026/01/3 | Accepted: 2026/05/13 | Published: 2026/09/11
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