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    ARIMA Model

    AI fundamentals & modelsAutoregressive integrated moving averageARIMA
    Editorially reviewed on: 1 source
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    The short answer · 10 words

    In short

    Statistical model for the analysis and forecasting of time series.

    Flow-alerta.cab (2026): ARIMA Model. https://flow-alerta.cab/en/glossar/arima-model. Editorially reviewed on: 09 October 2026.

    How much detail would you like?

    Practical

    Helps to build a comprehensible statistical comparison for time series forecasts.

    Example scenario: A company tests ARIMA for monthly demand values and evaluates forecasts at later periods of time.

    Check regularly collected data and evaluate forecasts using simple comparison methods and time-separated tests.

    At a glance

    Core
    ARIMA combines autoregressive components, differencing and a weighted combination of past error terms (the moving-average component). The parameters p, d and q control these components. Seasonal extensions are possible, but must be explicitly modeled.
    Use
    Helps to build a comprehensible statistical comparison for time series forecasts.
    Example
    Example scenario: A company tests ARIMA for monthly demand values and evaluates forecasts at later periods of time.
    Limits
    Use random train test splitting on time series; ignore structural breaks; confuse the moving average portion with a simple data average.

    In context

    Demand Forecasting

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

    Questions about this term

    Are forecasts guaranteed to be correct?

    No.

    Does each ARIMA model automatically record seasonality?

    No, this must be taken into account appropriately.

    All details and background

    ARIMA combines autoregressive components, differencing and a weighted combination of past error terms (the moving-average component). The parameters p, d and q control these components. Seasonal extensions are possible, but must be explicitly modeled.

    ELI5 – Explained simply

    The model examines past values and errors to estimate a further course.

    Why it matters in practice

    Helps to build a comprehensible statistical comparison for time series forecasts.

    Example

    Example scenario: A company tests ARIMA for monthly demand values and evaluates forecasts at later periods of time.

    Pitfalls and limits

    • Use random train test splitting on time series; ignore structural breaks; confuse the moving average portion with a simple data average.

    Origin & history

    ARIMA is an established statistical time series model. statsmodels documents ARIMA and seasonal variants such as SARIMA.

    Use in mid-sized companies

    • Check regularly collected data and evaluate forecasts using simple comparison methods and time-separated tests.

    Related terms

    Matching Flow-alerta service

    AI opportunity analysis & roadmapAI consulting: AI potential analysis & roadmap on tasks, data and prerequisites of a possible first AI use case.

    Matching tool / use case

    View use casesTool example: ARIMA in statsmodels.

    Sources

    Quellenprüfung: 09.10.2026. Definition und dokumentierte Einordnung abgeglichen; Beispiele beschreiben mögliche Anwendungen.

    Editorially reviewed on:

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