*Assistant Professor, Department of Business Management, CCS Haryana Agricultural University, Hisar, India
**Professor, Department of Business Management, CCS Haryana Agricultural University, Hisar, India, Email id: subodh.agarwal47@gmail.com
***Research Scholar, Guru Jambeshwar Science & Technology University, Hisar, India
Online published on 4 October, 2021.
A basic assumption in any TS analysis is that some aspects of the past pattern will continue to remain in the future. The most widely used technique for modeling and forecasting the TS data is Box-Jenkins’ Autoregressive integrated moving average (ARIMA) methodology. India's exports of agriculture goods will be modeled as ARIMA process. Identification of the values of parameters p,d and q is done on basis of ACF and PACF analysis. The Agricultural Export data was found to be non-stationary and differencing of order one was sufficient for getting an appropriate stationary series. In subsequent sections, we first present the data used and methodology applied for the model building. Next, Agricultural export estimation derived from the fitted models and the related discussions are given accordingly.
Arima, Trend, Forecast, Agricultural Export, Box and Jenkins
