Safflower (Carthamus tinctorius L.) yield forecasting in India - an application of Auto Regressive Integrated Moving Average (ARIMA) model

Abstract

The present study was carried out for forecasting the safflower productivity of India by fitting univariate Auto Regressive Integrated Moving Average (ARIMA) models. The data on safflower yield collected from 1965-66 to 2013-14 has been used for present study. The order of an ARIMA model is usually denoted by the notation ARIMA (p,d,q), where p is the order of the autoregressive part; d is the order of the differencing; q is the order of the moving-average process. For different values of p and q, various ARIMA models were fitted and appropriate model was chosen corresponding to minimum value of Akaike information criteria (AIC), Schwarz-Bayesian information criteria (SBC). ARIMA (1, 1, 2) model was found suitable for all India safflower yield with minimum MAPE (5.4).