Forecasting Pomegranate Prices and Arrivals in Himachal Pradesh: Insights from SARIMA Models and Seasonal Indices
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Author: Sahil Verma, Anju Sharma, Rakesh Kumar Gupta, Ashu Chandel, Satish K. Sharma, Chandresh Guleria and Vishal Thakur
Author Address: Department of Basic Sciences, Department of Plant Pathology and3Department of Social Sciences, Dr.Y. S.Parmar University of Horticulture and Forestry, Nauni, Solan-173230 (Himachal Pradesh)
Keywords: Box-Jenkins methodology, forecasting, market, time series, trends.
JEL Codes: C10, C22, C52, C53, Q11.
Abstract
This study examined the Pomegranate price trends and arrival patterns in key markets (Kangra, Dhanotu, Paonta Sahib) across Himachal Pradesh from 2007 to 2023 using monthly data. Prices rose significantly, while arrivals grew steadily. Seasonal peaks in arrivals occurred in August (118.28 per cent), September (160.94 per cent), and November (128.38 per cent) across the respective markets, whereas price surges were highest in May (110.85 per cent), March (115.14 per cent) and February (110.07 per cent). Price instability was moderate in Kangra and Paonta Sahib but high in Dhanotu; arrivals exhibited high instability in all markets. The coefficient of variation (CV)aligned with the coefficient of variation of deviations from the ideal (CDVI) for both metrics. For forecasted, optimal SARIMA Models for prices were SARIMA (2,1,1) (2,0,0)[12], SARIMA (0,1,3) (2,1,0)[12], and SARIMA (1,1,1)(2,0,1)[12],while arrivals were best predicted by SARIMA (1,1,1) (0,0,1)[12], SARIMA (0,0,4) (0,1,1)[12] with drift, and ARIMA (0,1,3). The findings can aid in understanding market dynamics and improving predictive accuracy. The findings highlighted the need to strengthen market intelligence, post-harvest infrastructure, and forecasting-based decision support systems to improve pomegranate market efficiency and stabilize farmer incomes.
Description
Indian J Econ Dev, 2026, 22(3), 515-525
https://doi.org/10.35716/IJED-25173