Implementation of Time Series Stochastic Modelling for Zea mays Production in India

Please use this identifier to cite or link to this item: http://localhost:8080/xmlui/handle/123456789/661

Full metadata record

DC Field	Value	Language
dc.contributor.author	Jai Sankar, T.	-
dc.contributor.author	Pushpa, P.	-
dc.date.accessioned	2023-06-24T14:46:44Z	-
dc.date.available	2023-06-24T14:46:44Z	-
dc.date.issued	2022-08-19	-
dc.identifier.uri	http://localhost:8080/xmlui/handle/123456789/661	-
dc.description.abstract	This study deals with the implementation of time series stochastic modelling for Zea mays (Maize) production in India during the years from 1951 to 2018. The demand for maize is spiralling in India. Maize can be grown in all seasons viz., Kharif (monsoon), post monsoon, Rabi (winter) and spring. The study considers Autoregressive (AR), Moving Average (MA) and ARIMA processes to select the appropriate ARIMA model forZea mays production in India. Based on ARIMA (p,d,q) and its components Autocorrelation Function (ACF), Partial Autocorrelation Function(PACF), Root Mean Square Error (RMSE), Mean Absolute Percentage Error (MAPE), Normalized BIC and Box-Ljung Q statistics estimated, ARIMA (0,1,1) was selected. Based on the chosen model, it could be predicted that Zea mays production would increase to 32.12million tons in 2025 from 27.02million tons in 2019 in India.	en_US
dc.language.iso	en	en_US
dc.subject	ARIMA	en_US
dc.subject	BIC	en_US
dc.subject	Forecasting	en_US
dc.subject	MAPE	en_US
dc.title	Implementation of Time Series Stochastic Modelling for Zea mays Production in India	en_US
dc.type	Article	en_US
Appears in Collections:	Department of Statistics

Files in This Item:

File	Description	Size	Format
540-Article Text-863-1-10-20220824.pdf		576.6 kB	Adobe PDF	View/Open

DSpace JSPUI