Application of Grey Model for Forecasting Rice Prices

Abstract

Aim: To forecast modal paddy prices in the three zones of Telangana (Northern, Southern, and Central) using the GM (1,1) grey model, addressing the challenge of limited long-term price data and providing a reliable tool for stakeholders to manage price volatility. Study Design: A forecasting study applying the GM (1,1) grey prediction model to annual price data. Place and Duration of Study: The study was conducted across the three zones of Telangana, India, using annual paddy price data from 2014 to 2024. Methodology: Annual modal price data for paddy from Telangana’s Northern, Southern, and Central zones were collected. The GM (1,1) grey model, suitable for small and uncertain datasets, was applied to each zone’s data to estimate model parameters. Forecast accuracy was evaluated using Mean Absolute Percentage Error and Root Mean Squared Error. Price forecasts were generated for the years 2025 to 2027. Results: The GM (1,1) model produced accurate forecasts with MAPE values below 10% for all zones. Forecasts indicate a consistent upward trend in modal paddy prices across Telangana from 2025 to 2027. Conclusion: The GM (1,1) grey model effectively forecasts short-term paddy prices in Telangana despite limited data, serving as a valuable decision-making tool for farmers and policymakers to anticipate price fluctuations and plan interventions accordingly.