Statistical modelling and forecasting annual sugarcane production in India: Using various time series models

• Published in: Annals of applied biology 2023-05, Vol.182 (3), p.371-380
• Main Authors: Tyagi, Sanjay, Chandra, Shalini, Tyagi, Gargi
• Format: Article
• Language: English
• Subjects: ARIMA; Autoregressive models; Empirical analysis; Error analysis; Food processing industry; Food security; Forecasting; Forecasting techniques; Mathematical models; Model accuracy; Naïve method; Performance measurement; Population growth; Root-mean-square errors; simple exponential smoothing model; statistical modelling; Statistical models; Sugarcane; sugarcane production; Time series; time series models
• ISSN: 0003-4746; 1744-7348
• DOI: 10.1111/aab.12825

This paper proposes a comparison of various time series forecasting models to forecast annual data on sugarcane production over 63 years from 1960 to 2022. In this research, the Mean Forecast Model, the Naive Model, the Simple Exponential Smoothing Model, Holt's model, and the Autoregressive Integrated Moving Average time series models have all been used to make effective and accurate predictions for sugarcane. Different scale‐dependent error forecasting techniques and residual analysis have been used to examine the forecasting accuracy of these time series models. SE of Residuals, Root Mean Squared Error (RMSE), Mean Absolute Error (MAE) and Akaike's Information Criterion (AIC) are used to analyse the forecast's accuracy. The best model has been selected based on the predictions with the lowest value, according to the three‐performance metrics of RMSE, MAE, and AIC. The estimated sugarcane production shows an increasing trend for the next 10 years and is projected to be 37,763.38 million tonnes in the year 2032. Further, empirical results support the plan and execution of viable strategies to advance sugarcane production in India to fulfil the utilisation need of the increasing population and further improve food security. This paper proposes a comparison of various time series forecasting models to forecast annual data on sugarcane production over 63 years from 1960 to 2022. In this research, the Mean Forecast Model, the Naive Model, the Simple Exponential Smoothing Model, Holt's model, and the Autoregressive Integrated Moving Average time series models have all been used to make effective and accurate predictions for sugarcane.