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.

农产品价格预测在有效规划和制定行政决策中起着重要作用。在这项研究中，已经尝试将自回归分数积分移动平均（ARFIMA）模型应用于大米的每日印度全部最高，最低和模态批发价格数据，以便捕获观察到的长期持久性。所考虑的价格序列是固定的，但是价格数据中大量存在长存储空间。因此，应用ARFIMA模型获取预测，并借助相对平均绝对百分比误差，均方根误差和平均绝对误差对预测进行基于窗口的评估。为此，