2023, Vol. 8, Issue 4, Part A
Development of hybrid model for improvement of forecast Agricultural commodity price
Author(s): Borsha Neog, Bipin Gogoi and AN Patowary
Abstract: Auto regressive fractional integrated moving average (ARFIMA) is widely applied for time series forecasting in long memory for divergent domain from several decades. The major limitation of this model is presumption of linearity. In real world, most of the long memory time series data are not purely linear, therefore hybrid model is evolved to enhance the prediction ability of ARFIMA models by fusing with other non-linear models. With this reasoning, this present study attempts to predict the price of Onion using ARFIMA-ANN and ARFIMA-SVM models. Results of this experiment justified the results of hybrid model gives better results with comparison to linear and non-linear models.
Pages: 08-17 | Views: 383 | Downloads: 36Download Full Article: Click Here
How to cite this article:
Borsha Neog, Bipin Gogoi, AN Patowary. Development of hybrid model for improvement of forecast Agricultural commodity price. Int J Stat Appl Math 2023;8(4):08-17.
