PRICE FORECASTING OF BAJRA (PEARL MILLET) IN RAJASTHAN: ARIMA MODEL

V.K. VERMA1*, S.S. JHEEB2, PRADEEP KUMAR3, S.P. SINGH4
1Department of Agricultural Economics, SKN College of Agriculture, Jobner-303329, India
2Department of Agricultural Economics, SKN College of Agriculture, Jobner-303329, India
3Department of Agricultural Economics, SKN College of Agriculture, Jobner-303329, India
4Department of Horticulture, SKN College of Agriculture, Jobner-303329, India
* Corresponding Author : verma2008eco@gmail.com

Received : 16-01-2016     Accepted : 26-02-2016     Published : 21-03-2016
Volume : 8     Issue : 9       Pages : 1103 - 1106
Int J Agr Sci 8.9 (2016):1103-1106

Keywords : Bajra, price forecasting, ARIMA model, Rajasthan
Academic Editor : Georgios Tsaniklidis, Rishikesh Mandloi, Mahadevakumar S
Conflict of Interest : None declared
Acknowledgements/Funding : None declared
Author Contribution : None declared

Cite - MLA : VERMA, V.K., et al "PRICE FORECASTING OF BAJRA (PEARL MILLET) IN RAJASTHAN: ARIMA MODEL." International Journal of Agriculture Sciences 8.9 (2016):1103-1106.

Cite - APA : VERMA, V.K., JHEEB, S.S., KUMAR, PRADEEP, SINGH, S.P. (2016). PRICE FORECASTING OF BAJRA (PEARL MILLET) IN RAJASTHAN: ARIMA MODEL. International Journal of Agriculture Sciences, 8 (9), 1103-1106.

Cite - Chicago : VERMA, V.K., S.S. JHEEB, PRADEEP KUMAR, and S.P. SINGH. "PRICE FORECASTING OF BAJRA (PEARL MILLET) IN RAJASTHAN: ARIMA MODEL." International Journal of Agriculture Sciences 8, no. 9 (2016):1103-1106.

Copyright : © 2016, V.K. VERMA, et al, Published by Bioinfo Publications. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution and reproduction in any medium, provided the original author and source are credited.

Abstract

The price behaviour of a commodity plays crucial role in farm level crop production planning. In this paper, an attempt has been made to forecast Pearl millet price using statistical time-series modelling techniques- Autoregressive Integrated Moving Average (ARIMA) Models. The forecasting performance of these models has been evaluated and compared by using common criteria such as: mean absolute percentage error, Akaike Information Criteria (AIC) and Schwarz's Bayesian Information criterion (SBC). The data used in this study include monthly wholesale price of bajra from January 2003 to December 2014. Among all the models tried, the Box-Jenkins ARIMA model (2, 1, 0) was best fit with least AIC (1557.22), SBC (1566.10) and MAPE (5.78). ARIMA (2, 1, 0) model is constructed based on autocorrelation and partial autocorrelation. Finally, forecasts were made based on the model developed. On validation of the forecasts from these models, ARIMA (2, 1, 0) model performed better than the others for bajra in Renwal market. The validation percentage ranged between 87.33 per cent in December 2015 to 96.67 per cent in July 2015. Thus, ARIMA model can be used to predict the future price of bajra in Renwal market of Rajasthan.