Forecasting Baselines, Temporal Validation, and Backtesting › Disadvantages

The main disadvantage is structural. A naive forecast assumes that the future conditional mean is close to the last observation. A seasonal-naive forecast assumes that the seasonal pattern repeats exactly. A drift forecast assumes that a linear trend estimated from two endpoints continues unchanged. These assumptions can fail under promotions, price changes, supply constraints, holidays, regime changes, and product life cycles. The three rules as defined here do not use covariates such as price, weather, or marketing spend. Covariate-based simple benchmarks are possible, but they are different forecasting rules.

Baselines also provide no built-in uncertainty model. We can construct empirical intervals from backtest errors, but those intervals inherit the dependence structure of the validation design. If we evaluate daily origins with a 28-day horizon, consecutive forecast errors overlap heavily. The number of error rows can look large while the number of independent forecast experiments is much smaller. Treating correlated errors as independent can misstate uncertainty. The direction and size depend on their covariance structure.

The validation itself has costs and pitfalls. Rolling-origin backtesting repeats forecast production and scoring. Model refitting follows the deployment policy and can add substantial cost. Expanding windows reuse all history, which may be inappropriate after a structural break. Rolling windows discard old data, which reduces sample size and can increase variance. A fixed seasonal period may be wrong after calendar changes. Percentage-error measures can explode near zero, and a single zero-demand day can make MAPE undefined. These are not arguments against baselines; they are reasons to report the validation design and the error scale explicitly.