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    Exponential Smoothing

    AI fundamentals & modelsETS models
    Editorially reviewed on: 1 source
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    The short answer · 15 words

    In short

    Exponential smoothing is a family of time series methods with decreasing weights for older observations.

    Flow-alerta.cab (2026): Exponential Smoothing. https://flow-alerta.cab/en/glossar/exponential-smoothing. Editorially reviewed on: 10 October 2026.

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    Practical

    The processes can provide comprehensible baselines for demand and capacity forecasts and their suitability is checked on time-separated data.

    Hypothetical example: Monthly spare parts quantities are estimated using seasonal ETS model and forecast intervals.

    SMEs can use ETS as a transparent baseline before more complex models.

    At a glance

    Core
    Variations take into account level, trend and seasonality. Statistical ETS models provide not only point forecasts but also forecast intervals; suitable components and error assumptions must be selected.
    Use
    The processes can provide comprehensible baselines for demand and capacity forecasts and their suitability is checked on time-separated data.
    Example
    Hypothetical example: Monthly spare parts quantities are estimated using seasonal ETS model and forecast intervals.
    Limits
    Ignore structural breaks, actions and sell-offs; choose model without time series backtest; report only point forecasts.

    In context

    Exponential Moving Average (EMA)

    EMA smoothes a time series and weights recent observations more strongly.

    Demand Forecasting

    A demand forecast estimates future demand for defined products, services, locations and time periods.

    Questions about this term

    Is EMA the same?

    EMA corresponds to simple smoothing, ETS includes further components.

    Does it work on every row?

    No.

    All details and background

    Variations take into account level, trend and seasonality. Statistical ETS models provide not only point forecasts but also forecast intervals; suitable components and error assumptions must be selected.

    ELI5 – Explained simply

    Younger observations count more strongly, while trend and season can be considered separately.

    Why it matters in practice

    The processes can provide comprehensible baselines for demand and capacity forecasts and their suitability is checked on time-separated data.

    Example

    Hypothetical example: Monthly spare parts quantities are estimated using seasonal ETS model and forecast intervals.

    Pitfalls and limits

    • Ignore structural breaks, actions and sell-offs; choose model without time series backtest; report only point forecasts.

    Origin & history

    Simple exponential smoothing was completed in the mid-20th century. Century developed; Holt and Winters expanded trend and season.

    Use in mid-sized companies

    • SMEs can use ETS as a transparent baseline before more complex models.

    Related terms

    Matching Flow-alerta service

    AI opportunity analysis & roadmapAI consulting: Check simple forecasting procedures and time-separated validation for your needs.

    Matching tool / use case

    View use casesUse Case: Seasonal demand forecast with backtesting

    Sources

    Quellenprüfung: 10.10.2026. Definition und dokumentierte Einordnung abgeglichen; Beispiele beschreiben mögliche Anwendungen.

    Editorially reviewed on:

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