ASSESSMENT OF TIME SERIES MODELS FOR FORECASTING RICE PRODUCTION IN KERALA AND INDIA: ARIMA VERSUS HOLT’S EXPONENTIAL SMOOTHING
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Authors
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SMITHA P
Department of Economics, Govt. College Chittur (University of Calicut), Palakkad - 678104, Kerala.
Keywords:
ARIMA, Forecasting, Holt’s Exponential Smoothing Model, Production, Rice, Time SeriesAbstract
The study conducted in 2023-24 compared two univariate time series forecasting models, ARIMA and Holt’s Exponential Smoothing (HES), to predict rice production in India and Kerala from 1980-81 to 2022-23. The models were evaluated based on various model accuracy measures like Mean Absolute Error (MAE), Mean Absolute Scaled Error (MASE), Root Mean Squared Error (RMSE), Mean Absolute Percent Error (MAPE), and Akaike Information Criterion (AIC), with results showing that the ARIMA model had higher accuracy than HES. The forecast for India predicted steady growth, from 135,687.7 in 2024 to 150,444.4 in 2028, with HES slightly higher than ARIMA. Similarly, in Kerala, HES forecasted a higher increase, from 569.68 in 2024 to 581.08 in 2028, compared to ARIMA, which showed slightly lower values across the period. Overall, ARIMA
demonstrated better predictive performance over HES for rice production.
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