International Journal of Agriculture and Food Science

International Journal of Agriculture and Food Science

Vol. 1, Issue 3, Part A (2019)

Application of time series modelling in price forecasting of agricultural commodities

Author(s):

Subrat Kumar Mahapatra, Dr. Abhiram Dash

Abstract:
Time series modelling is a dynamic research, which mostly aims to carefully collect and rigorously study the past observations of a time series to develop an appropriate model which describes the inherent structure of the series. This model is then used to generate future values for the series, i.e. to make forecasts. Time series forecasting thus can be termed as the act of predicting the future by understanding the past Forecasting the price of agriculture commodity such as vegetables, fruits (Horticultural crops) cereals, pulses, oilseeds (Agricultural crop) etc. is important related to economic concerned, farmer perspective, Agriculturist and Industrialist. Price forecasting help famers to take effective decision regarding market price (mandi price) or selling price of their crop, which crop to grow to earn profit, ultimately improve the condition and income of famer and also helps policy maker for agriculture decision. For forecasting area, production & productivity of agricultural crops, mostly ARIMA (Autoregressive integrated Moving average) Model is used but in case of price forecasting of agricultural crops ANN (Artificial Neural Network) is used. Neural Network approaches are applied in the field of agriculture for price forecasting in both short term and long terms Large amount of data related to commodity price, daily market price, arrival price is available. Neural approach with fuzzy can be used and also neuro fuzzy system may help in future for future price forecasting of commodity.

Pages: 27-29  |  29 Views  11 Downloads

How to cite this article:
Subrat Kumar Mahapatra, Dr. Abhiram Dash. Application of time series modelling in price forecasting of agricultural commodities. Int. J. Agric. Food Sci. 2019;1(3):27-29.