Exploring the Efficacy of Various Machine Learning Approaches for Gold Price Forecasting : Insights and Analysis

The increasing prominence of Artificial Intelligence (AI) or machine learning in data analysis, particularly in Gold price forecasting, is explored in this article. With a specific focus on Autoregressive Integrated Moving Average (ARIMA), Gaussian Process Regression (GPR) Model, Long Short-Term Memory (LSTM), Extreme Gradient Boosting (XGBoost), Light Gradient Boosting Machine (LightGBM) Model, and Categorical Boosting (CatBoost), the research delves into AI's transformative impact across various sectors globally, including agriculture, industry, and medicine. Utilizing datasets sourced from Kaggle encompassing daily gold rates from January 2013 to January 2024. The findings highlight the effectiveness of AI techniques in analyzing stock market behavior, providing valuable insights into forecasting methodologies and performance metrics such as Mean Absolute Percentage Error (MAPE), Accuracy, and Mean Absolute Error (MAE) across different algorithms. The analysis is conducted using Python in Google Colab, emphasizing the practical application of AI in enhancing forecasting accuracy and informing investment decisions in the gold market.