Copyright © 2025 Authors retain the copyright of this article. This article is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

@article{175041,
        author = {Agilan A and SaravanaKumar M.P and Abdul Mafaz M and DR.M.Ganesan},
        title = {A Survey on Gold Price Forecasting Model using Machine Learning Techniques},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {11},
        number = {11},
        pages = {1455-1465},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=175041},
        abstract = {Gold has long been a valuable asset and a crucial financial instrument, influencing global markets and economies. Accurately predicting gold prices is essential for investors, policymakers, and financial analysts to mitigate risks and make informed decisions. Traditional forecasting methods often struggle with the complex, nonlinear nature of gold price fluctuations, which are influenced by macroeconomic factors, geopolitical events, and market sentiment. Machine learning (ML) has emerged as a powerful tool for gold price prediction, offering data-driven insights and improved accuracy. This survey provides a comprehensive review of machine learning techniques applied to gold price forecasting, covering regression models, neural networks, ensemble methods, and hybrid approaches. We discuss commonly used datasets, preprocessing techniques, and evaluation metrics such as RMSE, MAE, and R². Additionally, we compare the strengths and limitations of different ML models, highlighting key challenges such as data quality, model interpretability, and real-time prediction. Finally, we explore future research directions, emphasizing the potential of deep learning, explainable AI, and alternative data sources in enhancing prediction accuracy. This study aims to guide researchers and practitioners in selecting appropriate ML models and methodologies for gold price forecasting.},
        keywords = {Gold price prediction, machine learning, forecasting, financial markets, time series analysis, deep learning},
        month = {April},
        }