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

SAFFLOWER YIELD FORECASTING IN INDIA - AN APPLICATION OF ARIMA MODEL


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Authors

  • K ALIVELU ICAR-Indian Institute of Oilseeds Research, Rajendranagar, Hyderabad-500 030, Telangana State
  • P PADMAVATHI ICAR-Indian Institute of Oilseeds Research, Rajendranagar, Hyderabad-500 030, Telangana State
  • C SARADA ICAR-Indian Institute of Oilseeds Research, Rajendranagar, Hyderabad-500 030, Telangana State
  • P LAKSHMAMMA ICAR-Indian Institute of Oilseeds Research, Rajendranagar, Hyderabad-500 030, Telangana State
  • M SANTHA LAKSHMI ICAR-Indian Institute of Oilseeds Research, Rajendranagar, Hyderabad-500 030, Telangana State

https://doi.org/10.56739/jor.v33i1.139055

Keywords:

ARIMA model, Forecasting, Safflower, Yield

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).

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References

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Submitted

2023-07-10

Published

2016-04-18

How to Cite

K ALIVELU, P PADMAVATHI, C SARADA, P LAKSHMAMMA, & M SANTHA LAKSHMI. (2016). Safflower (Carthamus tinctorius L.) yield forecasting in India - an application of Auto Regressive Integrated Moving Average (ARIMA) model: SAFFLOWER YIELD FORECASTING IN INDIA - AN APPLICATION OF ARIMA MODEL. Journal of Oilseeds Research, 33(1). https://doi.org/10.56739/jor.v33i1.139055