Unlocking Future Commodities Markets: Innovative Approaches to Price Prediction Using Supervised Machine Learning

Abstract

This study discovers the application of managed machine learning methods for expecting commodity prices, addressing the limitations of outdated forecasting approaches. By incorporating varied data sources, containing historical prices, macroeconomic signs, and geopolitical aspects, the study employs a range of machine learning systems such as Gradient Boosting, Support Vector Machines, and Deep Learning replicas. The research validates that these advanced methods can considerably enhance predicting accuracy equated to conventional methods. Through robust model training procedures, including cross-validation and hyper parameter tuning, the study recognizes key features and advances the projecting power of commodity price replicas. The results emphasize the potential of machine learning to offer more reliable and vibrant estimates in the face of market unpredictability and external worries. This effort provides valued understandings for merchants, stakeholders, and legislators, educating decision-making and risk administration in complex and erratic product markets.