Modelling and Forecasting of Tomato Prices in Jabalpur District of India
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Authors
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Shanya Tiwari
Jawaharlal Nehru Krishi Vishwavidyalaya, Jabalpur
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Umesh Singh
Jawaharlal Nehru Krishi Vishwavidyalaya, Jabalpur
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Kuldeep Rajpoot
Jawaharlal Nehru Krishi Vishwavidyalaya, Jabalpur
Keywords:
Tomato prices; Hybrid SARIMA-GARCH; Forecasting; Box-Cox transformation; Agricultural Market Volatility; ARIMA.Abstract
Tomato price volatility exerts a substantial influence on food inflation and poses significant challenges for agricultural market volatility management.
This study employs a hybrid SARIMA–GARCH modeling framework with Root Mean Square Error (RMSE) 0.3011, Mean Absolute Percentage
Error (MAPE) 3.1754, and Mean Absolute Error (MAE) 0.2356,to forecast monthly tomato prices for a horizon of 12 months in the Jabalpur market,
capturing both seasonal patterns and volatility clustering in the price series. The SARIMA(1,1,1)(1,0,0)12 model effectively captures seasonal dynamics,
yielding a RMSE of 0.385, MAPE of 3.812, and MAE of 0.286. The GARCH(1,1) component addresses conditional heteroskedasticity in the residuals.
To enhance model robustness, a Box–Cox transformation is applied on the price series to stabilize the variance and mitigate heteroscedastic effects.
Parameters are estimated using Maximum Likelihood (ML) and a two-stage Conditional Sum of Squares followed by ML (CSS–ML) procedure.
Comparative evaluation demonstrates that the hybrid SARIMA(1,1,1)(1,0,0)12–GARCH(1,1) model outperforms both SARIMA models with varying
specifications and ARFIMA (1,0.4916,1) models in terms of predictive accuracy. The hybrid model is further validated through residual diagnostics.
An exploratory extension incorporating log-transformed arrivals as exogeneous variables in a SARIMAX framework yields marginal improvements,
reinforcing the preference for model parsimony. The proposed hybrid approach offers a scalable and empirically validated tool for improving short
term price forecasts, with practical implications for production planning, inventory management, and policy formulation in volatile tomato markets.
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