FAMILY_TEMPORAL_DERIVATIVES - Experiment Results

Overview

This document tracks experimental runs for temporal derivative analysis using NDVI/EVI velocity, acceleration, and inflection point detection to predict potato prices. Tests whether advanced derivative analysis can surpass the 20.5% improvement achieved by basic temporal trajectory analysis.

Experimental Status

• Status: 🎯 READY FOR IMPLEMENTATION
• Created: 2025-08-20
• Foundation: Builds on FAMILY_GROWING_SEASON_DYNAMICS 20.5% temporal breakthrough
• Innovation: Rate of change analysis beyond static vegetation values
• Target: 23-28% improvement over strongest baseline
• Priority: High - Most advanced temporal satellite intelligence

Foundation Success

FAMILY_GROWING_SEASON_DYNAMICS Achievement: - Breakthrough Performance: 20.5% improvement with temporal trajectory analysis - Key Innovation: Growing season curve patterns contained genuine predictive signal - EVI vs NDVI Success: Multi-index temporal comparison revealed hidden relationships - Validated Approach: Real satellite data with proper baseline methodology

Temporal Derivative Innovation

Core Hypothesis: Rate of change patterns (velocity/acceleration) contain more predictive information than static vegetation values.

Physics-Inspired Insight: Just as velocity and acceleration tell us more about motion than position alone, vegetation growth rates and acceleration reveal crop development dynamics invisible to static NDVI/EVI values.

Data Validation

• ✅ Zarr store available: lake_31UFU_small.zarr (36GB, 5-day revisit for derivatives)
• ✅ High temporal frequency: 5-day Sentinel revisit enables detailed derivative calculation
• ✅ Multi-year coverage: 2015-2024 for robust temporal pattern identification
• ✅ Price data accessible: BoerderijApi NL.157.2086 weekly prices
• ✅ Field boundaries: BRP parcel data for precise derivative calculation
• ✅ Standard baselines: All 4 standard baselines (persistent, seasonal_naive, ar2, historical_mean) ready

Advanced Derivative Analysis Strategy

Temporal Intelligence Evolution

Moving from static values → trajectories → derivatives:

1. Static Analysis: Mean NDVI values (basic satellite intelligence)
2. Trajectory Analysis: Growing season curves (20.5% breakthrough)
3. Derivative Analysis: Velocity, acceleration, inflection points (target: 28%)

Mathematical Framework

• First Derivative (Velocity): dNDVI/dt = growth rate at each time point
• Second Derivative (Acceleration): d²NDVI/dt² = change in growth rate
• Inflection Points: Where d²NDVI/dt² = 0 (critical transitions)
• Higher Derivatives: Jerk and snap for complex temporal patterns

Performance Targets

• Variant A (23% target): Velocity and acceleration analysis
• Variant B (25% target): Inflection point detection and transition analysis
• Variant C (28% target): Seasonal momentum and multi-order derivative patterns

Technical Innovation

• Noise-Resistant Derivatives: Savitzky-Golay filtering for stable derivatives
• Gap Interpolation: Cubic spline for missing observations
• Multi-Scale Analysis: Derivatives at different temporal windows
• Cross-Index Derivatives: Compare velocity patterns between NDVI, EVI

Experiment Results: [TO BE UPDATED AFTER IMPLEMENTATION]

Data Versions: - Satellite data: lake_31UFU_small.zarr (5-day revisit, 2015-2024) - Price data: BoerderijApi NL.157.2086 - Parcel data: BRP consumption potato boundaries - Git SHA: [TO BE FILLED]

Derivative Analysis Results: - [TO BE FILLED AFTER IMPLEMENTATION]

Statistical Tests: - DM test vs strongest baseline: [TO BE FILLED] - Temporal derivative significance: [TO BE FILLED] - Cross-validation results: [TO BE FILLED] - Baseline comparison: ALL 4 standard baselines tested

Baseline Comparison: - Model: MAE = [TO BE FILLED] - Persistent baseline: MAE = [TO BE FILLED] (improvement: [TO BE FILLED]) - Seasonal naive baseline: MAE = [TO BE FILLED] (improvement: [TO BE FILLED]) - AR2 baseline: MAE = [TO BE FILLED] (improvement: [TO BE FILLED]) - Naive baseline: MAE = [TO BE FILLED] (improvement: [TO BE FILLED]) - Strongest competitor: [TO BE IDENTIFIED] - Primary improvement: [TO BE CALCULATED] vs [strongest_baseline_name]

Decision Log - 2025-08-20

Summary: FAMILY_TEMPORAL_DERIVATIVES prepared for implementation to explore rate-of-change intelligence beyond basic temporal analysis.

Key Innovation Decisions: 1. Physics-Inspired Approach: Apply velocity/acceleration concepts to vegetation analysis 2. Derivative Focus: Rate of change more informative than static values 3. Multi-Order Analysis: First, second, and higher-order derivatives 4. Inflection Detection: Critical transition points in vegetation development 5. Noise Management: Robust derivative calculation with proper smoothing

Implementation Strategy: - Variant A: Velocity/acceleration of NDVI and EVI (23% target) - Variant B: Inflection point detection and transition analysis (25% target) - Variant C: Seasonal momentum and multi-order derivatives (28% target)

Technical Requirements: - High-frequency temporal sampling (5-day Sentinel revisit) - Noise-resistant derivative calculation methods - Gap interpolation for missing observations - Multi-scale temporal analysis windows

Mathematical Framework: - Savitzky-Golay filtered derivatives for noise reduction - Cubic spline interpolation for gap filling - Cross-index derivative comparison (NDVI vs EVI rates) - Seasonal momentum and persistence analysis

Success Criteria: - Exceed 23% improvement over strongest baseline - Beat FAMILY_GROWING_SEASON_DYNAMICS 20.5% temporal record - Demonstrate clear interpretation of derivative patterns - Achieve practical significance for growth rate intelligence

Expected Impact: - Most advanced temporal satellite intelligence approach - Physics-inspired analysis revealing hidden temporal patterns - Rate-of-change intelligence for precision market timing - Foundation for real-time crop development monitoring

Status: ✅ READY FOR DERIVATIVE INTELLIGENCE IMPLEMENTATION

Codex Validation — 2025-11-10

Files Reviewed

• hypothesis.yml
• hypothesis.md
• experiment.md

Findings

1. No executable implementation. This family only contains documentation; there is no run.py, dataset builder, or notebook. Consequently no real-data ingestion, no model training, and no baseline comparison exist to substantiate the claims.
2. No evidence of real-data usage. Although the documents reference “REAL DATA ONLY,” the repository holds zero code or logs showing that Boerderij, Eurostat, or any other sources were accessed.
3. Price-only baseline is untouched. Without any experiment runs, we cannot assess whether the proposed derivative features outperform a simple price model.

Verdict

NOT VALIDATED – The family is still a proposal stage; until an actual experiment is implemented and executed with real data and standard baselines, the hypothesis remains unvalidated.