Comparative Efficacy of Holt-Winters, ETS and Croston Methods in Multi-Industry Rolling-Origin Backtesting

Operational demand forecasting rarely fails because a single algorithm is weak; it fails because one algorithm is imposed on heterogeneous series whose statistical shapes differ. We study a dependency-free forecasting engine that implements the classical exponential-smoothing family (simple exponential smoothing, Holt's linear trend, and additive Holt-Winters — the point-forecast equivalents of the additive error-trend-seasonal, or ETS, models), ordinary-least-squares regression with trend, seasonal dummies and exogenous drivers, and Croston's method for intermittent demand, together with seasonal-naive and moving-average baselines. Rather than committing to one model, the engine performs per-series rolling-origin (walk-forward) backtesting and selects a champion by lowest held-out error — Weighted Absolute Percentage Error (WAPE) where it is defined, mean absolute error where it is not — retaining challengers for transparency. We apply the engine across five commercial domains with deliberately different operational cadences: brewery, direct-to-consumer (DTC) e-commerce and discrete manufacturing on weekly data with a 13-week (one-quarter) operational horizon; software-as-a-service (SaaS) recurring revenue and education enrolment on monthly data with a 12-month horizon. Running the engine as published on 30 September 2026 over the 31 benchmark series produces a pattern that is interpretable at the three-step selection horizon: additive Holt-Winters wins where annual seasonality is strong and at least two seasonal cycles of history exist (8 of 31 series); Holt's linear trend wins for compounding recurring revenue where no genuine annual season exists (5 series: a distributed spirit and four of the five SaaS segments; the fifth chooses simple exponential smoothing); regression captures clean trending series, including structurally declining lines (5 series); robust local-level models (moving average, simple exponential smoothing) win 8 series, most of them lines whose hold-out window falls in a flat stretch of their seasonal cycle; and seasonal-naive wins 4. Croston's method wins one of the three lines flagged intermittent, for reasons the backtest exposes: on a Q4 gift bundle, whose zeros are almost all scheduled, seasonal-naive is best, or tied for best, over every window tested; on a limited seasonal release, whose in-season arrivals are random, Croston has the lowest absolute error on the hold-out; and on a spares line generated as random batches over a non-zero baseline, Croston reduces to simple exponential smoothing. The pattern is also fragile: every series is judged on just six periods, 17 of the 31 champions hold only a narrow lead by the engine's own test, and changing only the backtest horizon or the number of origins changes between 13 and 20 of the 31 assignments. We argue that the operational horizon and bucket must be matched to the domain's decision cadence and lead-time structure, not chosen for statistical convenience; that backtest-driven per-series selection is the honest way to let each series declare its own model, provided its margins and sensitivity are reported with it; and that the selection horizon should match the operational one. All reported figures are computed by executing the reference engine on semi-synthetic benchmark datasets whose seasonal shapes are anchored to public sources and whose operational detail is clearly-labelled simulation; they should therefore be read as demonstrating selection behaviour under known generating processes, not as measured accuracy on proprietary operational data. Preprint; not yet peer reviewed.