학술논문

Predictive Analysis of Market trends in Agriculture using ML/AI Techniques
Document Type
Conference
Source
2024 IEEE Applied Sensing Conference (APSCON) Applied Sensing Conference (APSCON), 2024 IEEE. :1-4 Jan, 2024
Subject
Engineering Profession
General Topics for Engineers
Analytical models
Uncertainty
Navigation
Time series analysis
Crops
Predictive models
Market research
Agriculture
Time Series Forecasting
ARIMA Modeling
Stationarity
Price Trends
Decision-Making
Risk Management
Crop Recommendations
Language
Abstract
The paper aims to predict crop prices to create a system that recommends crop choices, reduces price-related risks, and suggests optimal planting times. This paper approaches the problem of predicting crop prices by employing Time Series Fore-casting with a focus on ARIMA modeling. Our main objectives are to cut down on risks associated with price fluctuations and provide practical decision support for farmers, aiding in optimal crop choices and planting times. Starting with a detailed exploration of agricultural trends, we identify key factors influencing crop prices. We then dive into the application of advanced Time Series Forecasting techniques, specifically ARIMA modeling, to analyze historical monthly data for accurate predictions. The reliability of our models is ensured through thorough checks for stationarity. The paper emphasizes the crucial role of precise price forecasts in effective risk management for farmers. By integrating these insights into decision-making processes, farmers gain the ability to navigate market uncertainties and optimize their agricultural practices.