Gold Price Prediction System Using Machine Learning and Deep Learning Techniques

Abstract

Gold has long been considered one of the most reliable and valuable investment assets across the world. People invest in gold not only for financial security but also as a safeguard against inflation and economic uncertainty. However, the price of gold is highly dynamic and influenced by a variety of factors such as global economic conditions, inflation rates, currency fluctuations, geopolitical events, and market demand. This project presents a Gold Price Prediction System that forecasts future gold prices based on historical data and current market trends using advanced time-series forecasting techniques. Traditional statistical models like ARIMA (Auto Regressive Integrated Moving Average) are applied to identify linear patterns while modern deep learning models such as LSTM (Long Short-Term Memory) neural networks capture complex non-linear relationships. The system compares model performances using evaluation metrics and presents both historical and predicted prices through data visualizations, providing investors and financial analysts with an efficient, data-driven decision-support platform