shilohmasiimwe/Uganda-Grain-Price-Analysis-Forecasting-and-Prediction

This project analyzes Ugandan grain commodity prices and produces 12 month forecasts for 2026 using time series and ML models. It supports district and regional views and produces outputs for Power BI. # Uganda Grain Price Analysis and Forecasting This project analyzes Ugandan grain commodity prices and produces 12 month forecasts for 2026 using time series and ML models. It supports district and regional views and produces outputs for Power BI. ## Scope - Commodities: Maize, Sorghum White, Sorghum Red, Beans Yellow, Beans Nambaale, Beans Wairimu, Barley in highland districts, Soya - Geography: Central, Western, Northern, Eastern Uganda across 24 districts plus 3 barley only highland districts Kigezi, Kapchorwa, Kabale - Data: Daily retail prices in UGX per kg for Jan 2024 to Dec 2025 - Forecast horizon: Jan 2026 to Dec 2026, 12 months - Sources used for calibration: FEWS NET, WFP VAM, Agahikaine Grains, FAO GIEWS ## Methods in the notebook Section outline from Uganda_Grain_SARIMA_Analysis.ipynb: 1. Setup and data loading 2. Exploratory data analysis 3. Seasonal analysis 4. Stationarity tests and ACF and PACF 5. SARIMA modeling and forecasting with a custom implementation 6. SARIMAX with exogenous regional demand 7. Forecast visualization 8. Diagnostics and validation with backtesting 9. Performance summary 10. Final summary and forecast table 11. Machine learning and deep learning models including Random Forest, SVR, XGBoost, Prophet, and LSTM 12. Price change analysis for MoM, QoQ, and YoY 13. Appendix with model references and data sources ## Data and outputs - CSV datasets: PBI_Uganda_Grains_Daily.csv, PBI_Uganda_Grains_Monthly.csv, PBI_Uganda_Grains_Forecasts.csv, PBI_Model_Statistics.csv - Excel files: Uganda_Grain_Prices_Daily_2024_2025.xlsx, PowerBI_Dashboard_Guide.xlsx - Figures: outputs folder contains fig1_overview through fig19_price_change_summary and a backtest heatmap ## Data generation and maintenance scripts - rebuild_crops.py rebuilds the core CSVs using district multipliers and seasonal profiles - rebuild_excel_crops.py rebuilds the Excel deliverables - add_soya.py adds the so …