Abstract
This study applied a novel rough set combination approach for forecasting sugarcane production in India. The paper uses autoregressive integrated moving average (ARIMA), double exponential smoothing (DES) and Grey model (GM) to generate the single forecasts. Also, the weight coefficient is evaluated by underlying the rough set approach to combine the single forecasts obtained from different models. To validate our proposed analysis, Sugarcane from 1950 to 2011 was used for the overall empirical analysis and generate out-sample forecasts from 2012 to 2021 for the comparative analysis. Also, ARIMA (2, 1, 1) model is found more appropriate for forecasting Sugarcane production.
Highlights
India produces the largest amount of sugarcane and lands second on the list of top sugarcane-producing nations just after Brazil according to Foreign Agriculture Service (FSC) 2020
We study the comparative analysis of single time series and rough set combination methods by underlying mean absolute percentage error (MAPE) criterion
This paper applied a novel combination of forecasts by underlying rough set (RS) approach for the prediction of sugarcane production to India for the period of 1950 to 2011 in order to improve the performance of single models
Summary
The main objective of our study is to forecast sugarcane production in India using a novel rough set combination (RSC) approach
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