Static lab

MVP lab · temporal validation · decision support

ForecastOps

Operational forecasting with uncertainty: transparent baselines, temporal backtesting, interval fan charts and inventory what-ifs on synthetic demand.

Caveat: synthetic operational lab — not production financial forecasting, and not unattended purchase automation.

Demo dataset

365 synthetic daily observations for one SKU.

Forecast cockpit

Choose a baseline, inspect the fan chart, then read backtest metrics before trusting the curve.

Model comparison

Rolling temporal folds — never shuffled. Lower MAE wins for this demo SKU, not for every business.

Scenario simulator

Stress demand, then read order quantity, stockout risk and days of cover as decision cards — still human-reviewed.

Action plan

Methodology (interview-ready)

  1. Regularize daily sales and keep time order intact.
  2. Fit transparent baselines before any complex model.
  3. Score with rolling temporal folds (MAE, MAPE, RMSE, coverage).
  4. Communicate uncertainty with a horizon-widening interval.
  5. Translate forecast into inventory what-if cards with explicit limits.

Deep dive: docs/methodology.md · architecture: docs/ARCHITECTURE.md