Abstract: Forecasting of precious metal prices accurately is of crucial importance of an informed financial decision-making, robust risk mitigation and strategic asset allocation. This study represents a comparative analysis of time series forecasting methodologies including — Autoregressive Integrated Moving Average (ARIMA), Multilayer Perceptron (MLP), and Support Vector Regression (SVR) applied to the monthly historical datasets of gold and silver prices. These datasets were generated using OpenAI’s ChatGPT for academic purposes. These datasets are simulated and do not directly reflect real-world market data unless otherwise data is validated.  Each of the models is evaluated over a 24-month out-of-sample forecasting horizon using rigorous statistical metrics, including Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and Mean Absolute Percentage Error (MAPE). The empirical findings underscore the comparative advantages of data-driven machine learning approaches, particularly in capturing nonlinear and volatile dynamics, with MLP and SVR outperforming ARIMA in most scenarios. These results emphasize the increasing relevance of advanced machine learning techniques in financial time series modelling.

Simulated Data, Comparative Study, Metal, MLP, ARIMA, SVR, Time Series, forecasting

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