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Gold prices are non-linear, unexpected, volatile, and unregulated, making prediction difficult. Numerous studies have anticipated gold prices because they affect international economic and monetary systems. However, linear relationship studies rarely explain gold price changes. Gold price time series data is unpredictable, nonlinear, and volatile, making prediction difficult. Classical statistics and machine-learning (ML) approaches like Random-Forests, CNNs, and RNNs offer excellent accuracy but limits. A model that combines Temporal-Convolutional-Networks(TCN) with Query (Q) and Keys (K) attention-mechanisms (TCN-QV) is presented to improve gold price forecasts. The model extracts temporal properties from sequence data using stacked dilated causal convolution layers in the TCN architecture. To adapt weight distribution to information features, an attention mechanism is introduced. Finally, dense layers give projected results. This approach predicts Shanghai gold price time-series. The optimised model reduces Mean Absolute Error (MAE) by 5.47% in the least favorable case and 33.69% in the most favorable case across four investigational datasets compared to the baseline model.

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An Attention-Based Deep Learning Framework for Gold Price Prediction Using Time Series Data

  • 会议论文

  • 发表日期：

    2026年

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  • Abhijit T. Somnathe,
  • M. Parameshwari,
  • Sampurnima Pattem,
  • Sridhar N. Koka,
  • Shokhida Abdurakhmanova,
  • Pundru Chandra Shaker Reddy

摘要

Gold prices are non-linear, unexpected, volatile, and unregulated, making prediction difficult. Numerous studies have anticipated gold prices because they affect international economic and monetary systems. However, linear relationship studies rarely explain gold price changes. Gold price time series data is unpredictable, nonlinear, and volatile, making prediction difficult. Classical statistics and machine-learning (ML) approaches like Random-Forests, CNNs, and RNNs offer excellent accuracy but limits. A model that combines Temporal-Convolutional-Networks(TCN) with Query (Q) and Keys (K) attention-mechanisms (TCN-QV) is presented to improve gold price forecasts. The model extracts temporal properties from sequence data using stacked dilated causal convolution layers in the TCN architecture. To adapt weight distribution to information features, an attention mechanism is introduced. Finally, dense layers give projected results. This approach predicts Shanghai gold price time-series. The optimised model reduces Mean Absolute Error (MAE) by 5.47% in the least favorable case and 33.69% in the most favorable case across four investigational datasets compared to the baseline model.