1. Problem

A forecast can look acceptable on one headline metric and still be operationally dangerous. Teams need a way to decide whether a model is safe to ship, not just statistically convenient to present.

2. Approach

I defined forecast quality as a small set of auditable axes tied to failure modes: accuracy, bias, stability, tail robustness, and peak behavior.

  • Keep the metric set small enough to explain.
  • Tie each metric to a real operational failure mode.
  • Start with a thin-slice evaluation surface before building a platform.

3. Evidence

Forecast quality rollout flowchart
The staged rollout protected the team from turning evaluation into a second product before the core questions were answered.

4. Outcome

The framework made it much easier to compare models honestly and explain tradeoffs to commercial stakeholders who did not care about a metric zoo.

5. Tech stack

  • Python evaluation scripts for metric computation
  • Thin-slice reporting with quick plots and scorecards
  • Experiment tracking and artifact comparison

6. Useful links

7. Related reading

8. Call to action

If your team is still shipping forecasts off a single error metric, I can help design an evaluation surface that reflects operational reality.