Optimizing Precious Metal Price Forecasting with Hybrid Deep Learning Models: An Operational Research Perspective

application of seven deep neural network architectures in predicting
precious metal prices: LSTM, CNN, MLP, MLP-CNN, MLP-LSTM,
LSTM-CNN, and LSTM-CNN-LSTM. We will utilize a daily price
series over ten years for gold, silver, and platinum. Model
performances were evaluated using RMSE, MAPE, and RMSE
metrics. Results illustrate the strong performance of all neural
network models as such techniques will capture long-term
dependencies for better decision-making on the commodity markets.
As compared to the individual neural networks, the hybrid neural
network show superior performance. The LSTM-CNN-MLP model
is a very dependable and robust solution for precious metals
forecasting.This will clearly shows that NN plays a major role while
predicting the time series data. These results contribute to the wealth
of literature in operational research with improved predictive
accuracy in financial systems and portfolio optimization strategies.