Authors : T. Jai Sankar, R. Prabakaran, K. Senthamarai Kannan and S. Suresh
Abstract: 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.
T. Jai Sankar, R. Prabakaran, K. Senthamarai Kannan and S. Suresh, 2010. Stochastic Modeling for Cattle Production Forecasting. Journal of Modern Mathematics and Statistics, 4: 53-57.