Machine Learning Model to Predict the Prices of Agricultural Products

Abstract

We present an innovative machine learning model designed to forecast agricultural commodity prices, crucial for ensuring sustainable agricultural output. Our approach employs a sophisticated ensemble technique that combines the power of Auto-Regressive Integrated Moving Average (ARIMA) models with other complementary models. In the initial phase, individual ARIMA models are deployed to capture the intricate temporal patterns embedded in historical crop price data. As the project progresses, a dynamic adaptive ensemble framework comes into play, seamlessly integrating additional models to account for subtle variations in the dataset. This adaptive ensemble method is a key highlight, allowing our system to continuously evolve and respond to changing market dynamics effectively. By amalgamating diverse models, our framework significantly enhances prediction accuracy, outperforming standalone ARIMA models. Experimental results underscore the effectiveness of our proposed adaptive ensemble approach, showcasing superior predictive performance in comparison to traditional methods. Beyond its immediate impact on agricultural forecasting, this project establishes a scalable framework that goes beyond specific applications. It lays the groundwork for integrating and adapting diverse models in various time series prediction tasks, presenting a versatile solution for forecasting challenges across different domains. In summary, our project not only contributes to advancing agricultural forecasting capabilities but also pioneers a flexible and robust approach to time series prediction through adaptive ensemble methodologies.