Groundnut price forecasting using Auto Regressive Integrated Moving Average (ARIMA) model
GROUNDNUT PRICE FORECASTING USING AUTO-REGRESSIVE INTEGRATED MOVING AVERAGE MODEL
30 / 1
Authors
-
R VIJAYA KUMARI
College of Agriculture, Prof. Jayashankar Telangana Agril. University, Rajendranagar, Hyderabad-500 030, Telangana
-
VENKATESH PANASA
College of Agriculture, Prof. Jayashankar Telangana Agril. University, Rajendranagar, Hyderabad-500 030, Telangana
-
G RAMAKRISHNA
College of Agriculture, Prof. Jayashankar Telangana Agril. University, Rajendranagar, Hyderabad-500 030, Telangana
-
A SREENIVAS
College of Agriculture, Prof. Jayashankar Telangana Agril. University, Rajendranagar, Hyderabad-500 030, Telangana
Keywords:
ARIMA, Groundnut Prices, MAPE, Price forecast, StationarityAbstract
This paper analyzes the monthly modal prices of groundnut in Telangana using Autoregressive Integrated Moving Average (ARIMA) models to determine the most efficient and suitable model for forecasting these prices. The results show that the ARIMA (2,1,2) model was the most appropriate and effective for forecasting, based on comparison of model selection criteria and diagnostic tests, the Akaike Information Criterion (AIC), Bayesian Information Criterion (BIC), and Mean Absolute Percentage Error (MAPE), along with the significance of the (p,d,q) parameters. Time series analysis was conducted using SAS 9.3 software. The forecast indicated that during the rabi (yasangi) season, groundnut prices in the Gadwal market ranged from ` 6600-7000 per quintal (January to March 2025). The Minimum Support Price (MSP) for groundnut was ` 6783 per quintal for the 2024-25 marketing season. A comprehensive understanding of groundnut price trends and future projections will assist farmers and other stake holders in making informed decisions regarding buying and selling patterns. Therefore, the government should implement suitable policies to support the sector. This highlights the need for the government to adopt appropriate measures to ensure that both farmers and end users benefit from price stability
Downloads
References
Bharathi R, Havaldhar YN, Megeri SN and Patil GM 2009. Forecasting of arrivals and prices in Ramnagar and Siddlagatta market. Journal of the Indian Society of Agricultural Statistics, 63(3): 247-257.
Delvadiya JB, Chaudhary GK and Marviya PB 2023. Evaluation of statistical models for forecasting area, production and productivity of groundnut in Gujarat. Journal of Oilseeds Research, 40(3): 143-150.
Kaur S and Sharma S 2024. Unravelling the evolutionary trajectory: Exploring the dynamics of oilseeds cultivation in Haryana 1989-90 to 2020-21. Journal of Oilseeds Research, 41(2) : 156-163.
Kumari R Vijaya, Venkatesh Panasa, Ramakrishna G and Sreenivas A 2019. Agricultural market intelligence center - A case study of chilli crop price forecasting in Telangana. International Research Journal Agriculture Economics and Statistics, 10(2): 257-261.
Paul RK and Das MK 2010. Statistical modeling of inland fish production in India. Journal of the Inland Fisheries Society of India, 42(2): 1-7.
Prajneshu, Ravichandran S and Savita 2002. Structural time series models for describing cyclical fluctuations. Journal of the Indian Society of Agricultural Statistics, 55(1): 70-78.
SAS Institute Inc. 2023. SAS/ETS® User's Guide, Version 9.4: PROC ARIMA. Cary, NC: SAS Institute Inc.
Telangana State Marketing Department (n.d.). Retrieved from http://tsmarketing.in
