This paper explores effective methods for predicting gold prices, proposing three modeling strategies: a standalone Long Short-Term Memory (LSTM) network, a Convolutional Self-Attention (CSA) Network, and a combination model of both. Through empirical analysis, we systematically compare the performance of these three models in gold price forecasting, with a focus on evaluating their predictive accuracy. The experimental results indicate that the LSTM-Attention combination model significantly outperforms the standalone LSTM and Convolutional Self-Attention (CSA) Network in terms of prediction accuracy, demonstrating a more comprehensive ability to capture the dynamic features of price fluctuations. This model not only showcases the effective integration of LSTM and attention mechanisms in time series forecasting but also provides a practical tool for financial decision-making, offering valuable insights for investors in a volatile market environment.
Cite this paper
Wu, J. and Liu, J. (2026). Gold Price Prediction Based on LSTM-Attention Combined Model. Open Access Library Journal, 13, e14759. doi: http://dx.doi.org/10.4236/oalib.1114759.
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