📊 SRE & Platform Engineering — Capacity Planning Toolkit Demo

• Load Forecasting — Linear regression, seasonal decomposition, double exponential smoothing, all implemented from scratch
• Resource Utilization Tracking — Fleet-wide CPU/memory/disk monitoring with anomaly detection via z-score analysis
• Scaling Advisor — Headroom-based scaling recommendations with tier-specific policies and cost-aware decisions
• Cost Modeling — Unit economics computation, scaling cost projections, and optimization opportunity identification
• Auto-Method Selection — Forecaster automatically picks the best algorithm based on data characteristics

python -m unittest tests.test_forecaster -v

from src.forecaster import Forecaster, TimeSeriesPoint
from datetime import datetime, timedelta

# Load your historical utilization data
data = [
    TimeSeriesPoint(timestamp=datetime(2024, 1, 1) + timedelta(days=i), value=cpu_pct)
    for i, cpu_pct in enumerate(your_daily_cpu_data)
]

forecaster = Forecaster()
result = forecaster.auto_forecast(data, horizon_days=90)

print(f"Method selected: {result.method}")
print(f"Model fit (R²): {result.r_squared:.3f}")
for pt in result.forecast_points[:7]:  # First week
    print(f"  {pt.timestamp.date()}: {pt.value:.1f}%")