In the present study, seasonal autoregressive integrated moving average (SARIMA) methodology has been applied for modelling and forecasting of monthly export of meat and meat products from India. Augmented Dickey-Fuller test has been used for testing the stationarity of the series. Autocorrelation (ACF) and partial autocorrelation (PACF) functions have been estimated, which have led to the identification and construction of SARIMA models, suitable in explaining the time series and forecasting the future export. The evaluation of forecasting of export of meat and meat preparations has been carried out with root mean squares prediction error (RMSPE), mean absolute prediction error (MAPE) and relative mean absolute prediction error (RMAPE). The residuals of the fitted models were used for the diagnostic checking. The best identified model for the data under consideration was used for out-of-sample forecasting along with the upper and lower 95 per cent confidence interval up to the year 2013.
