Predicting Agriculture Commodity Price Trends Using Ensemble Regression Approach

Abstract

Purpose: This study aims to improve the forecasting of agricultural commodity price trends by developing an ensemble approach that integrates multiple regression models. Addressing this research problem, it aims to provide more accurate predictions for farmers and dealers, leveraging Wholesale Pricing Index data and Indian rainfall patterns. Research Method: The research uses various regression models and competitive ensemble techniques utilizing Wholesale Pricing Index data and Indian rainfall information to forecast Agricultural Commodity Price trends. Findings and Values: Empirical evidence supports the efficacy of the proposed ensemble approach over traditional regression models in accurately capturing agricultural price fluctuations. The competitive ensemble method proves beneficial for the food and financial industries in making informed decisions. This research offers practical utility by presenting a more accurate ensemble approach for forecasting Agricultural Commodity Price trends, supporting strategic planning in the food and economic industries.