Modeling and forecasting of oilseed production of India through artificial intelligence techniques

SANTOSHA RATHOD; K N SINGH; S G PATIL; RAVINDRAKUMAR H NAIK; MRINMOY RAY; VIKRAM SINGH MEENA

doi:10.56093/ijas.v88i1.79546

Authors

SANTOSHA RATHOD ICAR-Indian Agricultural Statistics Research Institute, New Delhi 110 012
K N SINGH ICAR-Indian Agricultural Statistics Research Institute, New Delhi 110 012
S G PATIL ACRI, Tamil Nadu Agricultural University, Killikulam 628 252
RAVINDRAKUMAR H NAIK Department of Economics, VMSR Vastrad Arts, Science and VS Bellihal Commerce College, Hungund, Karnataka 587 118
MRINMOY RAY ICAR-Indian Agricultural Statistics Research Institute, New Delhi 110 012
VIKRAM SINGH MEENA Crop Science Division, ICAR Headquarters, Krishi Bhawan, New Delhi 110 001

https://doi.org/10.56093/ijas.v88i1.79546

Keywords:

ARIMA, NLSVR, Oilseed, TDNN, Time series

Abstract

Indian agriculture has made considerable progress in respect of principle food crops, but the performance in case of oilseed crops is so far not good as compared to food grains. Production of oilseeds and oils are not meeting the increasing demand for edible oils and this widening demand-supply gap has necessitated imports of edible oils. India is world's largest importer of oil seeds as it imports more than 50 percent of its total production. Forecasting is used to analyze the past and current behavior to forecasts the future oilseeds production which intern provide an aid to decision-making and in planning for the future effectively and efficiently. Autoregressive integrated moving average (ARIMA) model is the most widely used model for forecasting time series. One of the main drawback of this model is the presumption of linearity. To model the series which contains nonlinear patterns, the artificial intelligence techniques like time delay neural network (TDNN) and non-linear support vector regression (NLSVR) model are commonly employed. In this paper an attempt has been made to forecast the oilseed production of India using ARIMA, TDNN and NLSVR models. Empirical results clearly reveal that the artificial intelligence techniques outperformed the ARIMA model.

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