Forecasting Baselines, Temporal Validation, and Backtesting › Advantages

These three baseline formulas are transparent and avoid iterative model fitting. The seasonal period and history policy still require choices made from training evidence. Their accuracy and cost depend on the series and implementation. If a seasonal-naive forecast is already accurate enough for inventory planning, a complex model must justify its additional maintenance cost, monitoring burden, and failure modes. Baselines therefore provide a minimum acceptable standard rather than a decorative comparison.

They also provide a reference that can prompt a leakage audit. Suppose a gradient-boosted model reports an implausibly low error on next-day demand. Comparing it with a naive forecast at the same origins can prompt an information-availability audit. A rolling average of realized responses that includes an unavailable target-day outcome, a promotion flag unavailable at the forecast origin, or a scaler fitted using values released after that origin violates that origin's information boundary. These violations can distort the comparison even when they produce no accuracy gain. The gap can prompt an audit; a large legitimate improvement is possible, and the gap alone does not establish leakage. Baselines also help us audit the information available to each forecast.

Finally, baselines make horizon-specific reporting natural. A naive forecast may be reasonable one day ahead and poor four weeks ahead. Seasonal naive can be strong at multiples of the seasonal period and weaker between them. Drift can capture a persistent trend but can also extrapolate a temporary run of high values. Reporting error by horizon, with the number of origins and the forecast window stated explicitly, turns a single aggregate score into an interpretable forecast audit.