A Comprehensive Approach to Gold Price Prediction Using Machine Learning and Time Series Models

This paper provides a comprehensive analysis of gold rate fluctuations in the USA and introduces an innovative method for more accurately predicting gold price changes using advanced machine-learning techniques. The dataset consists of daily gold rate data from January 1, 1985, to September 8, 2023, expressed in this country’s national currency. However, for our research, we specifically utilized the data expressed in United States Dollars (USD). Several machine learning models, ARIMA, LSTM, FB Prophet, SVM, Random Forest, and XGBoost, were employed to model and predict these time series data. The predictive performance of these methods is compared, providing insights into their effectiveness in capturing non-linear and long-term trends in gold prices. The outcomes reveal how machine learning could guide financial time series forecasting better and bring to light the most powerful clues about the way gold prices are moving on the global stage.