Not AvailableIn recent times, forecasting of agricultural commodity price becomes a major issue. But in the context of forecasting of time series data
exhibiting Long-Range Dependence (LRD) becomes more complex with the fractional differencing value. In general, Autoregressive Fractionally
Integrated Moving Average (AFRIMA) model is widely used for time-series forecasting having long range dependency. It has been observed that
in many cases forecasting performance with ARFIMA model is not satisfactory. Therefore, Multi-scale Autoregressive (MAR) model based on
wavelets decomposition can be used as an alternative for time-series forecasting. In the present investigation, MAR model is estimated using wavelet
decomposition at level 6. Here, an attempt has been made to improve the forecasting performance of MAR model by inclusion of some extra
regressors (modified MAR model). Daily wholesale price data on coconut of Kerala market has been used for the illustration purpose. A comparative
study has been made for ARFIMA, MAR and modified MAR model in terms of Mean Squared Error (MSE) and Root Mean Squared Error (RMSE).
The empirical study reveals that forecasting ability of modified MAR model outperforms the other two methodologies in terms of lower MSE and
RMSE values.Not Availabl
