Neural-Networks-Based Gold Price Forecasting with Recursive Feature Elimination and Optuna Tuning

Background: Strategic resource planning is crucial for optimizing supply chain management and ensuring efficient operations. This study aims to enhance strategic planning in gold mines by leveraging advanced gold price forecasting models. By predicting future gold prices accurately, mining companies can better plan their extraction, processing, and distribution activities, thereby improving overall supply chain efficiency. Methods: Various advanced forecasting models are implemented, including backpropagation neural networks (BPNNs), convolutional neural network (CNN), long short-term memory (LSTM), bi-directional LSTM (Bi-LSTM), gated recurrent unit (GRU), and bi-directional GRU (Bi-GRU). The feature selection process is facilitated by recursive feature elimination (RFE), and Optuna is used to fine-tune neural network models. Evaluation is based on root mean squared error (RMSE), mean absolute error (MAE), and mean absolute percentage error (MAPE). Results: BPNN performed the best in terms of lowest RMSE (0.5928), MAE (0.4091), and MAPE (0.34%), whilst Bi-GRU was the poorest performer, as it achieved RMSE of 9.41, MAE of 8.1916, and MAPE of 6.94%. In addition, Optuna further improved each model’s accuracy, except CNN, where the performance slightly decreased. Conclusions: Advanced forecasting neural systems underperformed the standard backpropagation neural networks. In this regard, BPNN proved to be highly effective in forecasting gold price, providing critical managerial implications for navigating the dynamic and volatile gold market for gold mining companies and investors.