Price Forecasting of Coriander: Methodological Issues Burark S. S., Sharma Hemant Department of Agricultural Economics & Management, Regional College of Agriculture, M P University of Agriculture & Technology, Udaipur, Rajasthan Online published on 9 October, 2012. Abstract Forecasting of agricultural prices plays a crucial role in planning of farm level crop production. In this paper, the prices of coriander have been forecasted using statistical time–series modelling techniques, viz. Autoregressive Integrated Moving Average (ARIMA), Artificial Neural Network (ANN) and Exponential Smoothing Models. The forecasting performance of these models has been evaluated and compared by using common criteria such as mean square error, mean absolute percentage error and Akaike Information Criterion (AIC) and Schwarz's Bayesian Information Criterion (SBC). The data used in this study include monthly wholesale price of coriander for April 2000 to May 2011. An ARIMA (1,1,1) model has been constructed based on the autocorrelation and partial autocorrelation. Finally, forecasts have been made based on the model developed. On validation of the forecasts from these models, ARIMA (1,1,1) model has performed better than other models for coriander in the Kota market of Rajasthan Top Keywords ARIMA, ANN, exponential smoothing model, price forecasting, coriander, Kota. Top |