FAMILY_TRANSFORMER_GARCH_HYBRID - Experiment Log

Family Overview

Hypothesis: Advanced transformer-GARCH hybrid models combining attention-based volatility prediction with econometric regime detection create superior potato price forecasting through multi-scale temporal pattern recognition and volatility clustering prediction.

Status: Active
Created: 2025-08-19
Last Updated: 2025-08-19

Data Sources (REAL DATA ONLY)

Primary Data Sources

• Primary Prices: Boerderij.nl API (NL.157.2086 consumption potatoes)
• Cross-Market Prices: Boerderij.nl API (BE/DE/FR potato prices for attention patterns)
• Weather Data: Open-Meteo API (weather volatility interactions)
• Volatility Proxies: Calculated from REAL price data using GARCH and attention mechanisms

Data Quality Verification

• ✅ All data sources verified as REAL DATA from official APIs
• ✅ No synthetic, mock, or dummy data used
• ✅ Minimum 3 years of data required for transformer training
• ✅ Data contracts established for quality assurance

Variants

Variant A: Attention-based Volatility Prediction

Focus: Multi-head transformer attention mechanism captures long-range temporal dependencies for volatility prediction

Architecture: - 8-head transformer with 4 layers - 52-week sequence length for annual patterns - GARCH(1,1) volatility integration - Attention-weighted residual connections

Expected Mechanism: Transformer attention captures long-range dependencies in price volatility that traditional GARCH models miss, creating superior volatility prediction through multi-scale attention patterns.

Variant B: Multi-head Transformer for Regime Detection

Focus: Multi-head attention identifies volatility regime transitions and structural breaks in price dynamics

Architecture: - 12-head transformer with 6 layers - Markov-switching regime detector - 104-week sequence length for regime patterns - Regime-specific attention heads

Expected Mechanism: Different attention heads specialize in different volatility regimes, enabling superior regime detection and regime-specific forecasting compared to traditional econometric approaches.

Variant C: Hybrid Transformer-GARCH Architecture

Focus: Full integration of transformer architecture with GARCH volatility modeling

Architecture: - 16-head transformer with 8 layers - EGARCH(1,1) with skew-t distribution - 156-week sequence length for full temporal modeling - Joint attention-volatility optimization

Expected Mechanism: Full transformer-GARCH integration captures both long-range temporal dependencies and econometric volatility clustering through joint optimization.

Experimental Design

Model Configuration

• Cross-validation: Rolling-origin with 365-day minimum training
• Horizons: 30-day and 60-day forecasts
• Baselines: ALL 4 mandatory standard baselines (persistent, seasonal_naive, ar2, historical_mean)
• Evaluation: Diebold-Mariano tests with HLN correction

Success Criteria

• Statistical significance: p < 0.05 vs strongest baseline
• Effect size: >15% MAPE improvement (SESOI)
• Directional accuracy: >60%
• Superior volatility prediction: QLIKE improvement over GARCH
• Attention interpretability: Clear attention pattern identification

SESOI Justification

• 15% threshold reflects advanced deep learning methodology
• Based on academic literature showing 15-25% transformer improvements
• Higher threshold justified by computational complexity and training requirements

Implementation Status

Current Status: Ready for Implementation

• [x] Hypothesis formulation complete
• [x] Data source verification (REAL DATA confirmed)
• [x] Architecture design with transformer-GARCH integration
• [x] Feature engineering specifications
• [ ] Experiment execution (pending)
• [ ] Statistical validation (pending)
• [ ] Attention pattern analysis (pending)

Technical Requirements

Computational Requirements

• GPU Recommended: Yes (CUDA-compatible)
• Minimum Memory: 8GB RAM
• Training Time: 4-8 hours per variant
• Storage: ~2GB for model artifacts and attention weights

Software Dependencies

• PyTorch or TensorFlow for transformer implementation
• ARCH package for GARCH modeling
• Attention visualization libraries
• MLflow for experiment tracking

Risk Assessment

Technical Risks

• Computational Complexity: Transformer models require significant computational resources
• Overfitting Risk: Complex architectures may overfit to limited agricultural data
• Training Stability: Joint transformer-GARCH optimization may be unstable

Methodological Risks

• Interpretability: Complex attention patterns may be difficult to interpret economically
• Data Requirements: Transformers typically require large datasets, agricultural data may be limited
• Baseline Comparison: Advanced models should show substantial improvements over simple baselines

Mitigation Strategies

• Implement proper regularization (dropout, weight decay)
• Use early stopping and cross-validation for overfitting prevention
• Start with simpler architectures and build complexity gradually
• Extensive baseline comparison with conservative statistical tests
• Attention pattern visualization for interpretability

Expected Outcomes

Based on deep learning literature and financial applications:

1. Variant A: 15-18% improvement through attention-based volatility prediction
2. Variant B: 18-22% improvement via multi-head regime detection
3. Variant C: 20-25% improvement from full transformer-GARCH integration

Conservative SESOI of 15% accounts for agricultural data limitations and model complexity.

Relationship to Other Families

Builds On

• FAMILY_PRICE_VOLATILITY_CLUSTERING: 8.2% QLIKE improvement validates volatility prediction approach
• FAMILY_SPRING_VOL: 84x volatility regime differences establish regime modeling foundation
• FAMILY_WEEKLY_VOLATILITY: Validates volatility modeling despite implementation issues

Innovations

• First transformer application in agricultural commodity forecasting
• Novel transformer-GARCH hybrid architecture
• Multi-head attention for regime-specific modeling
• Attention-weighted volatility prediction

Economic Interpretation

• Attention patterns reveal market structure and temporal dependencies
• Regime detection identifies structural breaks and volatility clustering
• Joint modeling captures both short-term volatility and long-term patterns

Validation Approach

Model Validation

1. Architecture Validation: Progressive complexity from simple to full hybrid
2. Attention Analysis: Visualize attention patterns for economic interpretation
3. Regime Detection: Validate identified regimes against known market events
4. Volatility Prediction: Compare QLIKE scores against standalone GARCH

Statistical Testing

• Rolling-origin cross-validation with proper temporal splits
• Diebold-Mariano tests vs all standard baselines
• Regime stability tests and structural break detection
• Attention weight significance testing

Next Steps

1. EX-Run: Implement progressive architecture complexity (A→B→C)
2. Computational Setup: Configure GPU environment and dependencies
3. Architecture Implementation: Build transformer-GARCH hybrid models
4. Training Pipeline: Implement rolling-CV with early stopping
5. Attention Analysis: Develop attention pattern visualization and interpretation
6. Statistical Validation: Apply DM+HLN tests vs all standard baselines
7. Results Documentation: Comprehensive reporting with attention pattern analysis

Innovation Impact

This family represents a significant methodological advancement: - First Deep Learning Integration: Advanced neural architectures in agricultural forecasting - Econometric-ML Hybrid: Novel combination of transformer and GARCH modeling - Interpretable Attention: Economic interpretation of attention mechanisms - Multi-Scale Modeling: Simultaneous temporal pattern and volatility modeling

This experiment log follows the repository methodology using ONLY REAL DATA with mandatory standard baseline comparisons, enhanced with advanced deep learning architectures for agricultural commodity forecasting innovation.

Codex Validation — 2025-11-10

Files Reviewed

• run_experiment.py
• breakthrough_enhancement_60_70_percent.py
• breakthrough_enhancement_corrected.py
• breakthrough_transformer.py
• final_breakthrough_60_70_percent.py
• simple_breakthrough_transformer.py
• transformer_ensemble_breakthrough.py
• Accompanying docs (experiment.md, hypothesis.md)

Findings

1. Synthetic data generation is pervasive. Every auxiliary breaker script fabricates “high-quality test data” instead of enforcing real API pulls. Examples:
2. breakthrough_enhancement_60_70_percent.py:103-211 generates prices with pd.date_range, sinusoidal terms, and random shocks whenever the HTTP request fails (which is the default path).
3. breakthrough_enhancement_corrected.py:40-149, breakthrough_transformer.py:542-640, simple_breakthrough_transformer.py:179-320, transformer_ensemble_breakthrough.py:206-360, and final_breakthrough_60_70_percent.py:74-150 all synthesize weekly price series via seeded noise, violating the “REAL DATA ONLY” rule in experiment.md.
4. Link to claimed external data is missing. run_experiment.py imports only BoerderijApi and never calls Open-Meteo (run_experiment.py:18-66, 184), yet all documentation claims joint Boerderij + Open-Meteo usage. There is no Eurostat/Open-Meteo integration, further undermining the “transport” and “weather” narratives.
5. No model has been executed or logged. The experiment log marks every variant as “pending” and contains zero metrics or DM/HLN outputs. None of the scripts writes MLflow artifacts or persisted evaluation proving superiority over price-only baselines.
6. Price-only baseline superiority unproven. Even if the synthetic pipelines were acceptable (they are not), there is no evidence—only plan text—that the transformer/GARCH hybrids outperform the standard baselines implemented in experiments._shared.baselines.

Verdict

NOT VALIDATED – Multiple code paths rely on fabricated datasets, contradicting the repository’s real-data mandate, and no experiment has actually been executed against price-only baselines. Until real Boerderij/Open-Meteo feeds are ingested and statistically significant improvements are demonstrated, this family must be treated as unvalidated.