Applied Research on Gold Stock Price Prediction Based on Long Short-Term Memory Neural Network
DOI:
https://doi.org/10.54097/kt6q2418Keywords:
Long Short-Term Memory Neural Network (LSTM), Gold Stock Price Prediction, Time Series Analysis, Model Optimization, Financial Forecasting.Abstract
As financial markets become increasingly intricate, accurately predicting gold stock prices has become an indispensable decision - making tool for investors. This not only aids in risk management but also holds significant potential for profit - seeking in the volatile gold stock market. This paper proposes a gold stock price prediction model based on LSTM. By integrating multidimensional technical indicators such as Moving Average (MA) and Relative Strength Index (RSI), it optimizes feature engineering to effectively capture price fluctuation patterns. To tackle the hyperparameter sensitivity issue during LSTM training, an exhaustive search method is employed for hyperparameter optimization. This meticulous approach helps the model to better adapt to various market scenarios and historical data characteristics, thus enhancing its generalization ability. The optimized LSTM model shows remarkable improvements in performance metrics including Mean Squared Error (MSE), Root Mean Squared Error (RMSE), and Mean Absolute Error (MAE), validating its effectiveness in capturing both long - term trends and short - term volatility.
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