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International Journal of Current Microbiology and Applied Sciences (IJCMAS)
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National Academy of Agricultural Sciences (NAAS)
NAAS Score: *5.38 (2019)
[Effective from January 1, 2019]
For more details click here

ICV 2018: 95.39
Index Copernicus ICI Journals Master List 2017 - IJCMAS--ICV 2018: 95.39
For more details click here

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PRINT ISSN : 2319-7692
Online ISSN : 2319-7706
Issues : 12 per year
Publisher : Excellent Publishers
Email : editorijcmas@gmail.com / submit@ijcmas.com
Editor-in-chief: Dr.M.Prakash
Index Copernicus ICV 2017: 100.00
NAAS RATING 2018: 5.38

Int.J.Curr.Microbiol.App.Sci.2017.6(8): 2887-2895
DOI: https://doi.org/10.20546/ijcmas.2017.608.345


Maize Price Forecasting Using Auto Regressive Integrated Moving Average (ARIMA) Model
Venkatesh Panasa*, R. Vijaya Kumari, G. Ramakrishna and S. Kaviraju
Department of Agricultural Economics, College of Agriculture, Professor Jayashankar Telangana State Agricultural University, Hyderabad-500030, Telangana, India
*Corresponding author
Abstract:

This paper examined the monthly modal prices of maize using Autoregressive Integrated Moving Average (ARIMA) models so as to determine the most efficient and adequate model for analyzing the maize monthly modal prices in Telangana. The results indicate that Autoregressive Integrated Moving Average ARIMA 211 model is the most adequate and efficient model. This was ascertained by comparing the various model selection criterion and the diagnostic tests for various models among them Akaike Information Criteria (AIC) and Bayesian Information Criteria (BIC) and Mean Absolute Percentage Error (MAPE). Time series Analysis was done using SAS 9.3 software. A better understanding of maize price situation and future prices will facilitate farmers and end users to make appropriate decisions regarding buying and selling patterns hence government should take adequate policies. The forecasted results suggest that there are expectations of increasing maize prices in Badepalli market next five months (October to February). This requires the government to take appropriate measures to ensure that farmers and end user get benefited.


Keywords: Maize prices, Stationarity, Differencing, ARIMA, MAPE and Price forecast
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How to cite this article:

Venkatesh Panasa, R. Vijaya Kumari, G. Ramakrishna and Kaviraju, S. 2017. Maize Price Forecasting Using Auto Regressive Integrated Moving Average (ARIMA) Model.Int.J.Curr.Microbiol.App.Sci. 6(8): 2887-2895. doi: https://doi.org/10.20546/ijcmas.2017.608.345