Research on enhancing forecast accuracy of gold futures using LSTM neural networks across various time periods

Gold remains one of the most coveted commodities, with its futures prices reaching record highs. Predicting these prices accurately is crucial for investors and market managers. This study utilizes gold futures price data from 2002 to 2024 sourced from CSMAR. Applying an LSTM neural network model for forecasting, we optimized various time series structures, parameters, and features. Our findings indicate that the LSTM model effectively predicts daily trends in gold futures prices, achieving higher accuracy compared to annual predictions. However, the model's performance diminishes when forecasting monthly and quarterly prices, revealing instability and reduced precision in discontinuous time series. These results underscore the LSTM model's strength in accurately forecasting continuous daily data over intermittent periods.