TsegayIS122123/ethiopia-fi-forecasting-system

A forecasting system that tracks Ethiopia's digital financial transformation using time series methods and event impact modeling. # Ethiopia Financial Inclusion Forecasting System ## Overview A forecasting system that tracks Ethiopia's digital financial transformation using time series methods and event impact modeling. This project addresses the need to understand and predict financial inclusion trends in Ethiopia's rapidly evolving digital payment ecosystem. ## Objective Build a robust forecasting system that predicts Ethiopia's progress on two core dimensions of financial inclusion: 1. **Access** — Account Ownership Rate 2. **Usage** — Digital Payment Adoption Rate The system will help stakeholders understand: - What drives financial inclusion in Ethiopia - How events (product launches, policy changes, infrastructure investments) affect inclusion outcomes - Projections for 2025-2027 ## Features - **Unified Data Schema**: Consistent structure for observations, events, and impact relationships - **Event Impact Modeling**: Quantify effects of policies, product launches, and market entries - **Multi-Scenario Forecasting**: Generate optimistic, base, and pessimistic scenarios - **Interactive Dashboard**: Visual exploration of trends and forecasts - **Data Enrichment**: Framework for adding new data sources - **Validation Framework**: Compare forecasts against historical data ## Strategy 1. **Data First**: Start with comprehensive data exploration and enrichment 2. **Event-Driven**: Model financial inclusion as a function of market events 3. **Scenario-Based**: Account for uncertainty with multiple forecast scenarios 4. **Iterative Refinement**: Continuously improve models as new data becomes available ## Methods 1. **Time Series Analysis**: ARIMA, Exponential Smoothing, Prophet 2. **Regression Modeling**: OLS with intervention variables 3. **Event Impact Estimation**: Comparable country evidence + local validation 4. **Machine Learning**: Gradient boosting for non-linear relationships 5. **Ensemble Methods**: Combine multiple models for robust forecasts ### **Task 1: Data Exploration and …