Adaptive Intelligence

These features are unique to Vectrix — not available in statsforecast, Prophet, Darts, or any other forecasting library. Adaptive intelligence means your forecasts respond to changing conditions: detecting regime shifts, self-correcting as new data arrives, respecting business constraints, and profiling data DNA for intelligent model selection.

Regime Detection

Real-world data rarely follows a single pattern. Markets alternate between bull and bear, retail demand shifts between seasons, and business metrics change after policy updates. Vectrix detects these regimes automatically using Hidden Markov Models

from vectrix import RegimeDetector

detector = RegimeDetector(nRegimes=3)
result = detector.detect(data)

print(f"Current: {result.currentRegime}")
for label, stats in result.regimeStats.items():
    print(f"  {label}: mean={stats['mean']:.2f}, std={stats['std']:.2f}")

Regime-Aware Forecasting

Traditional forecasters use one model for all data. RegimeAwareForecaster identifies the current regime and uses the model that performed best during similar past regimes

from vectrix import RegimeAwareForecaster

raf = RegimeAwareForecaster()
result = raf.forecast(data, steps=30, period=7)
print(result.currentRegime)
print(result.modelPerRegime)

Self-Healing Forecast

Forecasts degrade over time. Self-healing monitors errors as actual values arrive and automatically adjusts remaining predictions

from vectrix import SelfHealingForecast

healer = SelfHealingForecast(predictions, lower, upper, historical_data)
healer.observe(actual_values)

report = healer.getReport()
print(f"Health: {report.overallHealth} ({report.healthScore:.1f}/100)")
print(f"Improvement: {report.improvementPct:.1f}%")

updated_preds, updated_lower, updated_upper = healer.getUpdatedForecast()

Constraint-Aware Forecasting

Statistical models don’t know about your business rules. Predictions can go negative, exceed capacity, or show unrealistic year-over-year swings. Constraint-aware forecasting applies domain knowledge as post-processing rules

from vectrix import ConstraintAwareForecaster, Constraint

caf = ConstraintAwareForecaster()
result = caf.apply(predictions, lower, upper, constraints=[
    Constraint('non_negative', {}),
    Constraint('range', {'min': 0, 'max': 5000}),
    Constraint('capacity', {'capacity': 10000}),
    Constraint('yoy_change', {'maxPct': 30, 'historicalData': past_year}),
])

Constraint Types

Forecast DNA

Every time series has a unique statistical signature. DNA profiling extracts 65+ features to create a deterministic fingerprint that drives intelligent model selection and difficulty estimation

from vectrix import ForecastDNA

dna = ForecastDNA()
profile = dna.analyze(data, period=7)
print(f"Fingerprint: {profile.fingerprint}")
print(f"Difficulty: {profile.difficulty} ({profile.difficultyScore:.0f}/100)")
print(f"Category: {profile.category}")
print(f"Recommended: {profile.recommendedModels}")
print(f"Summary: {profile.summary}")

!!! warning “Feature values are inside the features dict”

# CORRECT
profile.features['trendStrength']
profile.features['seasonalStrength']

# WRONG — AttributeError
profile.trendStrength
```