Abstract:
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 us...Show MoreMetadata
Abstract:
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.
Published in: 2025 IEEE International Conference on Interdisciplinary Approaches in Technology and Management for Social Innovation (IATMSI)
Date of Conference: 06-08 March 2025
Date Added to IEEE Xplore: 09 May 2025
ISBN Information: