Gold Price Prediction Based on LSTM-Attention Combined Model

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.