Advanced Price Forecasting for Food Commodities

Food price volatility is a global issue that profoundly affects food security, economic stability, and the livelihoods of millions, especially in developing regions. Essential commodities such as wheat, rice, and corn are central to global food systems but are highly vulnerable to unpredictable price fluctuations driven by climate variability, geopolitical tensions, and global trade dynamics. Accurately forecasting the prices of such commodities is crucial for stabilizing markets and enabling timely interventions. This study introduces an Advanced Price Forecasting System, combining Auto-Regressive Integrated Moving Average (ARIMA) and Long Short-Term Memory (LSTM) models. The dual-model approach allows the system to deliver reliable short- and long-term price predictions by leveraging the strengths of both statistical and deep learning methodologies. The ARIMA model captures linear trends effectively in the short term, while the LSTM model addresses nonlinear dependencies and complex seasonal patterns in long-term forecasts. The project integrates robust preprocessing techniques, interactive visualizations, and modular design, making it scalable for various commodities. The system includes a React-based dashboard for user interaction, enabling stakeholders to access insights dynamically. Initial evaluations reveal improved forecasting accuracy and the potential for actionable insights to enhance agricultural planning, policymaking, and consumer decision-making. Future work includes hybrid model integration, expansion to additional commodities, and incorporation of external data sources like weather and economic indicators.