mussiehaile/A-Time-Series-Forecasting-Model-for-Ethiopian-Commodity-Prices

# Time Series Forecasting Model for Ethiopian Commodity Prices ## 🌾 Overview This project presents a comprehensive machine learning approach to forecasting commodity prices in Ethiopia, specifically focusing on red onion prices. The work is conducted in collaboration with the **Agricultural Transformation Institute (ATI)** and the **Ethiopian Statistical Agency**, addressing critical challenges in agricultural price prediction for policy-making and market planning. ## 🎯 Problem Statement Commodity price forecasting is crucial for: - **Policy makers**: Planning agricultural policies and interventions - **Farmers**: Making informed planting and harvesting decisions - **Traders**: Managing inventory and pricing strategies - **Consumers**: Understanding price trends and planning purchases Traditional forecasting methods (ARIMA, Exponential Smoothing) struggle with: - **Non-linear relationships**: Complex interactions between multiple factors - **External factors**: Holidays, seasons, market shocks that don't follow simple patterns - **Feature engineering**: Limited ability to incorporate domain knowledge ## 🔬 Research Context While most commodity price forecasting research is conducted in regions like the **Middle East** and **China** (where extensive agricultural data infrastructure exists), this project brings advanced machine learning techniques to the Ethiopian context, adapting methodologies to local market dynamics, Ethiopian calendar systems, and regional holidays. **Key Research Papers & References:** - Chinese agricultural forecasting studies (often use ensemble methods with external features) - Middle Eastern commodity price prediction (focus on oil, grains, dates) - Time series forecasting with machine learning (LightGBM, XGBoost applications) ## 🏗️ Methodology ### Why Supervised Learning Over Traditional Forecasting Models? #### Limitations of ARIMA and Classical Methods: 1. **Linear Assumptions**: ARIMA models assume linear relationships, missing …