Forecasting Onion Production in India Using Arima Models: A Research Study

This study explores the application of ARIMA (AutoRegressive Integrated Moving Average) models for forecasting onion production in India. Accurate forecasting of agricultural production is essential for effective planning and decision-making in the agricultural sector. ARIMA models, which integrate autoregression, differencing, and moving average components, offer a robust methodology for time series forecasting. This study aims to explore the application of ARIMA models in forecasting onion production in India. By utilizing historical production data, the study will identify suitable ARIMA parameters and evaluate the model’s effectiveness in predicting future production levels. Use the fitted model to forecast future onion production. Evaluate the model's performance using metrics like Mean Absolute Error (MAE), Mean Squared Error (MSE), or Root Mean Squared Error (RMSE).The goal is to provide insights that can help stabilize onion supply and contribute to better planning and decision- making within the agricultural sector. Keywords: ARIMA,MAE,RMSE,MSE,SPSS