An application of time series ARIMA forecasting model for production of sugarcane in India

Abstract

livelihoods and industrial sectors such as sugar and ethanol manufacturing. The unpredictability in sugar cane output which is impacted by meteorological conditions, agricultural methods and economic policies, emphasises the importance of reliable forecasting models for planning and decision making the use of time series forecasting models notably the auto aggressive integrated moving average ARIMA model has grown in popularity in agricultural forecasting due to its ability to capture and predict complicated temporal trends. ARIMA models excel at managing non stationary data for differencing, making them ideal for analysing historical trends seasonal swings and other underlying patterns in sugar cane production data.