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Forecasting Sugarcane yield of India using ARIMA-ANN hybrid model
Author(s) -
Mrinmoy Ray,
R. S. Tomar,
V. Ramasubramanian,
Kehar Singh
Publication year - 2018
Publication title -
bhartiya krishi anusandhan patrika
Language(s) - English
Resource type - Journals
eISSN - 0976-4631
pISSN - 0303-3821
DOI - 10.18805/bkap100
Subject(s) - autoregressive integrated moving average , yield (engineering) , econometrics , autoregressive model , statistics , mathematics , computer science , time series , materials science , metallurgy
Sugarcane is one of the main cash crops of India hence forecasting sugarcane yield is vital for proper planning. Till date Autoregressive integrated moving average (ARIMA) model is a stand out amongst the most main stream approach for sugarcane yield forecasting. Recent research activity reveals that hybrid model improves the accuracy of forecasting when contrasted with the individual model. Along these lines, in this study, ARIMA-ANN hybrid model was utilized for forecasting sugarcane yield of India. The hybrid model was compared with ARIMA approach. Empirical results clearly reveal that the forecasting accuracy of the hybrid model is superior to ARIMA.

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