Arima Forecasting In Soymeal And Beyond: An Empirical Study Bridging The Path To Millet-Based Forecasting

Abstract

Soymeal, a rich source of protein and fiber, held paramount importance in livestock and poultry sectors. Derived post oil extraction from soybean seeds, its production and price dynamics hinged on multiple factors including seed supply-demand, crushing costs, transportation expenses, and competition from alternative feed constituents. The study aimed to employ the Autoregressive Integrated Moving Average (ARIMA) model to forecast soymeal production, import, export, and price trends globally for year 2023 by using data from 2016 to 2022. By combining historical insights with predictive analytics, the paper endeavored to illuminate the prospective trajectory of soymeal dynamics on a global scale, offering valuable insights for stakeholders. Moreover, it is noteworthy that the applicability of the ARIMA forecasting model extends beyond soymeal, encompassing other agricultural commodities such as millets and other minor grains. Millets, celebrated for their nutritional value and resilience, hold immense potential in addressing global food security challenges. The ARIMA model's capability to unravel temporal intricacies and patterns could likewise provide insights into millet production, pricing, and trade, contributing to the sustainable development of diverse agricultural sectors. This research not only enriches our understanding of soymeal dynamics but also underscores the versatility of the ARIMA model for forecasting a spectrum of agricultural commodities, promoting informed decision-making and strategic planning.