Forecasting Baselines, Temporal Validation, and Backtesting › Summary

Forecasting baselines are not a formality. Naive and seasonal naive have simple random-walk interpretations, while drift estimates the slope of a random walk with drift. They provide transparent benchmarks at matched origins, horizons and losses. The naive forecast repeats the last observation, the seasonal-naive forecast repeats the last seasonal cycle, and the drift forecast continues a linear trend. Each makes a specific assumption, and each fails in a specific way.

Temporal validation must respect the forecast origin, the horizon, and the information set. At origin (o), only information through (o) may be used. Rolling-origin backtesting advances the origin, produces forecasts for explicit future targets, and scores them after the targets are revealed. Expanding windows use all history; rolling windows forget old history. Overlapping target blocks can create dependent errors, so error-row count alone does not establish independence.

Forecast error should be reported by horizon, with MAE, RMSE, bias, and a scale-aware measure such as MASE. Percentage errors need explicit handling of zeros and near-zero targets. MASE needs a leakage-free seasonal scale. Empirical uncertainty intervals should be interpreted with the dependence of overlapping errors in mind. When these pieces are in place, a sophisticated forecasting model has a fair benchmark and a validation design that resembles the decision it is meant to support.