Predictive Analysis and Classification of Commodity Market Trends Using Enhanced RNN with Economic Indicator

In the rapidly evolving landscape of commodity markets, accurate forecasting and classification of price trends are crucial for informed decision-making by investors, traders, and policymakers. This study presents a novel predictive analysis framework utilizing an Enhanced Recurrent Neural Network (RNN) integrated with significant economic indicators to improve the accuracy of commodity market trend predictions. We leverage a comprehensive dataset comprising historical prices and various economic metrics, including inflation rates, interest rates, and geopolitical factors, sourced from Kaggle. The proposed Enhanced RNN architecture incorporates advanced techniques, such as attention mechanisms and dropout layers, to effectively capture the temporal dependencies and nonlinear relationships inherent in time-series data. By integrating economic indicators as additional features, our model gains a contextual understanding of market dynamics, allowing it to respond to fluctuations driven by economic conditions. We evaluate the performance of the Enhanced RNN against baseline models using metrics such as Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and classification accuracy. The results demonstrate that our model significantly outperforms traditional forecasting methods, achieving higher prediction accuracy and robustness in volatile market scenarios. This research contributes to the field of business analytics by providing a sophisticated tool for commodity price forecasting, highlighting the importance of economic indicators in predictive modeling, and offering insights into future market trends.