TY  - JOUR
T1  - Stochastic Modeling for Cattle Production Forecasting
AU - Jai Sankar, T. AU - Prabakaran, R. AU - Senthamarai Kannan, K. AU - Suresh, S. 
JO  - Journal of Modern Mathematics and Statistics
VL  - 4
IS  - 2
SP  - 53
EP  - 57
PY  - 2010
DA  - 2001/08/19
SN  - 1994-5388
DO  - jmmstat.2010.53.57
UR  - https://makhillpublications.co/view-article.php?doi=jmmstat.2010.53.57
KW  - AIC
KW  -Cattle production
KW  -BIC
KW  -ARIMA
KW  -forecasting
KW  -India
AB  - This study proposes a technique using Autoregressive Integrated Moving Average (ARIMA) Model for cattle production. Stochastic modeling and forecasting plays a vital role in many fields such as agricultural production, animal husbandry economics, stock prices prediction, etc. ARIMA Model was introduced by Box and Jenkins. Hosking has introduced a family of models called fractionally differenced autoregressive integrated moving average models by generalizing the d fraction in ARIMA (p, d, q) models. Mandal was using ARIMA Model for analyzing sugarcane production. This study analysis the design of ARIMA process to select the appropriate model for cattle production in Tamilnadu. These results are verified on the basis of various diagnostic checking and error analysis which is used to forecast the future values. Also, results are shown by graphically and numerically.
ER  -