Metrics that matter
MAPE vs WAPE, bias, and why segment-level evaluation prevents misleading averages.
Insights for forecasting teams
These guides explain forecasting in operational terms: which metrics to use, how to backtest without leakage, and how to make uncertainty usable. We write for planners, analysts, and operations leaders who want reliable improvements without hype.
Recommended reading order
If you are building or evaluating a forecasting system.
- 1Pick metrics and segments that match decisions
- 2Run a clean backtest with baselines
- 3Add uncertainty intervals and monitoring
Core topics
Each topic focuses on decisions. We avoid jargon where possible and use the same language planners use: horizons, segments, bias, service levels, and overrides.
Backtesting basics
How to test models across horizons without data leakage, and how to compare to baselines.
Uncertainty intervals
Turning forecast uncertainty into better inventory and staffing decisions with coverage checks.
Anomaly signals
Detecting unusual changes while controlling false positives and using human review effectively.
Planner workflows
Overrides, notes, approvals, and review routines that improve forecasts over time.
Monitoring & drift
What to track daily and weekly to keep a forecasting system stable in production.
A short glossary for teams
AI forecasting often sounds complicated because terms vary across tools. We use a simple vocabulary: a baseline is a reference method you can explain, a segment is a meaningful slice of the business, and drift is a measurable change that signals the model or data may no longer represent reality. Keeping terms consistent makes governance easier and helps stakeholders align on what success means.
Baseline
A simple reference forecast used to prove improvement.
Bias
Systematic over- or under-forecasting that can cause waste or stockouts.
Coverage
How often actuals fall inside a forecast interval.
Drift
A change in data or patterns that may reduce forecast quality.
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