2025, Vol. 10, Issue 5, Part A
Hybrid statistical model for forecasting production of maize crop in Karnataka State, India
Author(s): Sanketh Raj H, B Ramana Murthy, KN Sreenivasulu and V Sita Ram Babu
Abstract: The present study was carried out to apply the
Hybrid statistical model specifically Auto Regressive Integrated Moving Average
(ARIMA) combined with Time-Delay Neural Network (TDNN) on Maize production in
Karnataka state for the period of 1962 to 2022. The ARIMA (3, 1, 2) model were
found to be best forecasted model for Maize production by criteria, which
having highest R
2, low Akaiki Information criteria (AIC) and low
Root Mean Square Error (RMSE). Consequently, a nonlinear TDNN model was applied
to fit the residuals. For Maize production forecasting, the TDNN (4-15-1) model
with 4 input delays, 15 hidden neurons, and 1 output neuron demonstrated low
RMSE and low Mean Absolute Percentage Error (MAPE) ensuring high accuracy. The
ARIMA (3,1,2) model with TDNN (4-15-1) model on ARIMA residuals was found to be
the best forecasting model for Maize production of Karnataka state, India. Finally,
the hybrid model ARIMA with TDNN was improved the forecasting accuracy. The
forecasted results for Maize production for Karnataka state were shown an
increasing trend.
DOI: 10.22271/maths.2025.v10.i5a.2035Pages: 53-58 | Views: 866 | Downloads: 4Download Full Article: Click Here
How to cite this article:
Sanketh Raj H, B Ramana Murthy, KN Sreenivasulu, V Sita Ram Babu.
Hybrid statistical model for forecasting production of maize crop in Karnataka State, India. Int J Stat Appl Math 2025;10(5):53-58. DOI:
10.22271/maths.2025.v10.i5a.2035
