Price forecasting of pigeonpea: A case study of Kanpur Mandi of Uttar Pradesh
Keywords:
ACF, AIC, ARIMA, Forecast, PACF, PigeonpeaAbstract
The volatility of pulse prices, particularly pigeonpea, results in significant losses for farmers because of unexpected market conditions. This study aims to forecast pigeonpea prices to assist policy makers and farmers in making informed decisions regarding land allocation, marketing strategies and storage planning. The ARIMA model was used to assess the time series data of daily mandi prices from August 2002 to June 2024 to forecast harvest prices for the 2025 season in Uttar Pradesh State. Stationarity of the data was confirmed via the Augmented Dickey-Fuller test and the result was found to be significant at 5% level of significance. Based on the Auto Correlation Function (ACF) and Partial Auto Correlation Function (PACF), ARIMA (1,1,2) was the best-fitted model. The model’s dependability was assessed using the lowest Akaike Information Criterion (AIC) and Normalized Bayesian Information Criterion (BIC) values.
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