On March 18, 2026, the Philippine Food Price Prediction system underwent its largest upgrade since the initial Random Forest + PyTorch analysis was published on March 5, 2026. This post documents every change — what was added, what was improved, and a side-by-side comparison of the old and new capabilities.
Upgrade Timeline
Timestamp (PHT)
Component
Action
2026-03-18 07:30
Wave 1 — Core Infrastructure
Dark mode, PWA, Excel export, parallel training, caching, retry logic, API server, data quality dashboard, 136 initial tests
2026-03-18 08:15
Wave 2 — Advanced ML Models
LSTM deep learning, exogenous features (NOAA/Frankfurter/FAO), climate scenarios, early warning system, ensemble stacking, model comparison report
2026-03-18 09:00
Cross-Validation
4-worker cross-review: La Niña bug fix (climate_scenarios.py), ensemble NameError fix, TimeSeriesSplit→KFold correction. All 174 tests passing.
2026-03-18 10:30
Wave 3 — Documentation & Reporting
README rewrite (355 lines), model comparison report, blog post update, integration test suite (174/174 pass)
2026-03-18 12:00
Truth Verification
Cross-checked blog claims against actual code. Fixed 3 discrepancies. Live site verified.
Before vs. After — System Comparison
Model Architecture
Capability
Before (March 5)
After (March 18)
ML Models
Random Forest (500 trees) + PyTorch feedforward NN (2×50 hidden)
REST API (7 endpoints): /api/health, /api/commodities, /api/regions, /api/forecast, /api/data-quality, /api/alerts, /api/scenarios. 30s response caching. Port 8787.
Data Pipeline
Manual download + retrain
daily_update.py with SHA-256 change detection, exponential backoff retry (3 attempts), –dry-run validation, data quality gates (10 checks: min rows, required columns, null %, date range, positive prices)
Training
Sequential training, single model family
Parallel training across 5 model families × 5 variants (25 models per commodity). ProcessPoolExecutor for CPU parallelism. Per-commodity best model selection by validation MAPE.
Test Suite
No tests
174 automated tests across 4 files: data quality validation, model accuracy benchmarks, API endpoint testing, ensemble integration, exogenous feature pipeline integrity
Documentation
Basic README
355-line README with architecture diagrams, 15 sections, full API reference, installation guide, model specifications, quick start
The stacking ensemble combines three diverse tree-based learners through a regularized linear meta-learner:
Layer
Model
Trees
Max Depth
Key Config
Base 1
GradientBoosting
200
4
lr=0.05, subsample=0.8
Base 2
ExtraTrees
200
10
min_samples_leaf=5
Base 3
RandomForest
200
10
min_samples_leaf=3
Meta-Learner
Ridge Regression
—
—
α=1.0, 5-fold CV
Early Warning Thresholds
Detector
Low
Medium
High
Critical
Price Spike
>15%
>20%
>30%
>50%
Year-over-Year
>20%
>30%
>50%
>80%
Regional Z-Score
>1.5σ
>2.0σ
>2.5σ
>3.0σ
Model Divergence
>15%
>25%
>40%
>60%
REST API Endpoints (api_server.py)
Endpoint
Method
Purpose
/api/health
GET
Server health, uptime, data file status
/api/commodities
GET
73 commodities with categories, price ranges, record counts
/api/regions
GET
17 administrative regions with price averages
/api/forecast
GET
Forecasts per commodity (5 models), accuracy metrics
/api/data-quality
GET
Coverage stats, anomaly counts, model accuracy summary
/api/alerts
GET
Early warning alerts (filters: severity, type, commodity)
/api/scenarios
GET
ENSO climate scenario projections
Bugs Found & Fixed During Cross-Validation
Four independent AI agents cross-reviewed each other’s implementations. Three bugs were caught and fixed:
Bug
File
Found By
Fix
La Niña ONI mask inversion — oni_min < oni_max always true, yielding 0 La Niña observations
climate_scenarios.py:210–213
Alpha (reviewing Beta)
Corrected to oni_min <= oni <= oni_max with proper negative ranges
per_commodity NameError — undefined variable in stacking loop
ensemble_model.py
Delta (self-fix)
Added proper variable initialization before loop
TimeSeriesSplit incompatible with stacking — CV produced empty folds for small datasets
ensemble_model.py
Delta (self-fix)
Switched to KFold(n_splits=5, shuffle=False)
File Inventory (27 files, 51,238 lines)
File
Lines
Purpose
lstm_model.py
639
PyTorch LSTM deep learning price forecaster
ensemble_model.py
577
Stacking ensemble (GB + ExtraTrees + RF → Ridge)
exogenous_features.py
~500
NOAA ONI, USD/PHP, FAO FPI integration
climate_scenarios.py
~430
ENSO-price correlation & scenario projection
early_warning.py
~460
4-detector anomaly system + policy recommendations
model_report.py
465
Automated model comparison report generator
api_server.py
~300
REST API server (7 endpoints, stdlib only)
early_warning.html
~600
Interactive Leaflet.js alert dashboard
tests/ (4 files)
174 tests
Data quality, model accuracy, API, ensemble, exogenous
README.md
355
Architecture, installation, API reference, 15 sections
What’s Next
The following capabilities are planned for future releases:
Satellite imagery (NDVI) integration — Agricultural yield estimation from vegetation indices to predict supply-side shocks before they hit market prices
Real-time API with rolling forecasts — FastAPI-powered live forecast endpoint updating daily as new WFP data arrives
Philippine ePrice system integration — Direct connection to the Department of Trade and Industry’s automated price monitoring
Temporal Fusion Transformers — Multi-horizon forecasting with attention-based architecture for complex seasonal decomposition
Automated anomaly alerting — Push notifications when critical-severity alerts are triggered in real-time data
