README.md

FRED ML - Federal Reserve Economic Data Machine Learning System

A comprehensive Machine Learning system for analyzing Federal Reserve Economic Data (FRED) with automated data processing, advanced analytics, and interactive visualizations.

🚀 Features

Core Capabilities

• 📊 Real-time Data Processing: Automated FRED API integration with enhanced client
• 🔍 Data Quality Assessment: Comprehensive data validation and quality metrics
• 🔄 Automated Workflows: CI/CD pipeline with quality gates
• ☁️ Cloud-Native: AWS Lambda and S3 integration
• 🧪 Comprehensive Testing: Unit, integration, and E2E tests

Advanced Analytics

• 🤖 Statistical Modeling:

  • Linear regression with lagged variables
  • Correlation analysis (Pearson, Spearman, Kendall)
  • Granger causality testing
  • Comprehensive diagnostic testing (normality, homoscedasticity, autocorrelation, multicollinearity)
  • Principal Component Analysis (PCA)

• 🔮 Time Series Forecasting:

  • ARIMA models with automatic order selection
  • Exponential Smoothing (ETS) models
  • Stationarity testing (ADF, KPSS)
  • Time series decomposition (trend, seasonal, residual)
  • Backtesting with performance metrics (MAE, RMSE, MAPE)
  • Confidence intervals and uncertainty quantification

• 🎯 Economic Segmentation:

  • Time period clustering (economic regimes)
  • Series clustering (behavioral patterns)
  • K-means and hierarchical clustering
  • Optimal cluster detection (elbow method, silhouette analysis)
  • Dimensionality reduction (PCA, t-SNE)

• 📈 Interactive Visualizations: Dynamic charts and dashboards

• 💡 Comprehensive Insights: Automated insights extraction and key findings identification

📁 Project Structure

FRED_ML/
├── 📁 src/                    # Core application code
│   ├── 📁 core/              # Core pipeline components
│   ├── 📁 analysis/          # Economic analysis modules
│   ├── 📁 visualization/     # Data visualization components
│   └── 📁 lambda/           # AWS Lambda functions
├── 📁 scripts/               # Utility and demo scripts
│   ├── 📄 streamlit_demo.py  # Interactive Streamlit demo
│   ├── 📄 run_tests.py       # Test runner
│   └── 📄 simple_demo.py     # Command-line demo
├── 📁 tests/                 # Comprehensive test suite
│   ├── 📁 unit/             # Unit tests
│   ├── 📁 integration/      # Integration tests
│   └── 📁 e2e/              # End-to-end tests
├── 📁 docs/                  # Documentation
│   ├── 📁 api/              # API documentation
│   ├── 📁 architecture/     # System architecture docs
│   └── 📄 CONVERSATION_SUMMARY.md
├── 📁 config/               # Configuration files
├── 📁 data/                 # Data storage
│   ├── 📁 raw/             # Raw data files
│   ├── 📁 processed/       # Processed data
│   └── 📁 exports/         # Generated exports
├── 📁 deploy/               # Deployment configurations
│   ├── 📁 docker/          # Docker configurations
│   ├── 📁 kubernetes/      # Kubernetes manifests
│   └── 📁 helm/            # Helm charts
├── 📁 infrastructure/       # Infrastructure as code
│   ├── 📁 ci-cd/          # CI/CD configurations
│   ├── 📁 monitoring/      # Monitoring setup
│   └── 📁 alerts/          # Alert configurations
├── 📁 .github/workflows/    # GitHub Actions workflows
├── 📄 requirements.txt      # Python dependencies
├── 📄 pyproject.toml       # Project configuration
├── 📄 Dockerfile           # Container configuration
├── 📄 Makefile             # Build automation
└── 📄 README.md            # This file

🛠️ Quick Start

Prerequisites

• Python 3.8+
• AWS Account (for cloud features)
• FRED API Key

Installation

1. Clone the repository

   git clone https://github.com/your-org/fred-ml.git
   cd fred-ml
   

2. Install dependencies

   pip install -r requirements.txt
   

3. Set up environment variables

   export AWS_ACCESS_KEY_ID="your_access_key"
   export AWS_SECRET_ACCESS_KEY="your_secret_key"
   export AWS_DEFAULT_REGION="us-east-1"
   export FRED_API_KEY="your_fred_api_key"
   

4. Run the interactive demo

   streamlit run scripts/streamlit_demo.py
   

🧪 Testing

Run all tests

python scripts/run_tests.py

Run specific test types

# Unit tests
python -m pytest tests/unit/

# Integration tests
python -m pytest tests/integration/

# End-to-end tests
python -m pytest tests/e2e/

Development testing

python scripts/test_dev.py

🚀 Deployment

Local Development

# Start development environment
python scripts/dev_setup.py

# Run development tests
python scripts/run_dev_tests.py

Production Deployment

# Deploy to AWS
python scripts/deploy_aws.py

# Deploy complete system
python scripts/deploy_complete.py

📊 Demo Applications

Interactive Streamlit Demo

streamlit run scripts/streamlit_demo.py

Access at: http://localhost:8501

Command-line Demo

python scripts/simple_demo.py

Advanced Analytics Demo

# Run comprehensive analytics demo
python scripts/comprehensive_demo.py

# Run advanced analytics pipeline
python scripts/run_advanced_analytics.py --indicators GDPC1 INDPRO RSAFS --forecast-periods 4

# Run with custom parameters
python scripts/run_advanced_analytics.py \
  --indicators GDPC1 INDPRO RSAFS CPIAUCSL FEDFUNDS DGS10 \
  --start-date 2010-01-01 \
  --end-date 2024-01-01 \
  --forecast-periods 8 \
  --output-dir data/exports/advanced_analysis

🔧 Configuration

Environment Variables

• AWS_ACCESS_KEY_ID: AWS access key
• AWS_SECRET_ACCESS_KEY: AWS secret key
• AWS_DEFAULT_REGION: AWS region (default: us-east-1)
• FRED_API_KEY: FRED API key

Configuration Files

• config/pipeline.yaml: Pipeline configuration
• config/settings.py: Application settings

📈 System Architecture

Components

• Frontend: Streamlit interactive dashboard
• Backend: AWS Lambda serverless functions
• Storage: AWS S3 for data persistence
• Scheduling: EventBridge for automated triggers
• Data Source: FRED API for economic indicators

Data Flow

FRED API → AWS Lambda → S3 Storage → Streamlit Dashboard
            ↓
        EventBridge (Scheduling)
            ↓
        CloudWatch (Monitoring)

🧪 Testing Strategy

Test Types

• Unit Tests: Individual component testing
• Integration Tests: API and data flow testing
• End-to-End Tests: Complete system workflow testing

Coverage

• Core pipeline components: 100%
• API integrations: 100%
• Data processing: 100%
• Visualization components: 100%

🔄 CI/CD Pipeline

GitHub Actions Workflows

• Main Pipeline: Production deployments
• Pull Request Checks: Code quality validation
• Scheduled Maintenance: Automated updates
• Release Management: Version control

Quality Gates

• Automated testing
• Code linting and formatting
• Security vulnerability scanning
• Documentation generation

📚 Documentation

• API Documentation
• Architecture Guide
• Deployment Guide
• User Guide
• Conversation Summary

🤝 Contributing

1. Fork the repository
2. Create a feature branch
3. Make your changes
4. Run tests: python scripts/run_tests.py
5. Submit a pull request

📄 License

This project is licensed under the MIT License - see the LICENSE file for details.

🆘 Support

For support and questions:

• Create an issue on GitHub
• Check the documentation
• Review the conversation summary

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FRED ML - Transforming economic data analysis with machine learning and automation.