Abstract
Price forecasting of agricultural commodities plays an important role in efficient planning and formulation of executive decisions. In this study, an attempt has been made to apply autoregressive fractionally integrated moving average (ARFIMA) model to the daily all India maximum, minimum and modal wholesale price data of rice in order to capture the observed long-run persistency. The price series under consideration are stationary, but there is a significant presence of long memory in the price data. Accordingly, ARFIMA model is applied to obtain the forecasts and window-based evaluation of forecasting is carried out with the help of relative mean absolute percentage error, root mean square error and mean absolute error. To this end, a comparative study has also been made between the best fitted ARFIMA model and the best fitted ARIMA model and it is observed that ARFIMA model outperforms the usual ARIMA model.
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