Gold has long been regarded as a valuable asset and a reliable haven for
investors, particularly during periods of economic uncertainty. Gold price forecasting is
difficult due to the market's intrinsic volatility and sensitivity to various factors,
including global economic trends, inflation rates, currency value fluctuations, and
geopolitical events. This study presents a novel strategy for predicting gold prices that
combines Long Short-Term Memory (LSTM) neural networks and Particle Swarm
Optimization (PSO). Traditional models struggle with these complications of
uncertainty, but the PSO-LSTM technique improves LSTM hyperparameters for
greater accuracy. The PSO-LSTM model beats both standalone deep Learning models
and traditional forecasting, based on performance criteria such as Mean Absolute Error
(MAE), Root Mean Square Error (RMSE), and Mean Absolute Percentage Error
(MAPE).
Keywords: Deep learning, Gold price, LSTM, Machine learning, Optimization, Time series.
