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ARIMA Model Example

Time Series Energy Usage ARIMA Education Prediction
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This workflow predicts the irregular component of time series (energy consumption) by autoregressive integrated moving average (ARIMA) models that aim at modeling the correlation between lagged values and controling for seasonality in time series. The number of lagged values considered in the model can be set manually, or it can be optimized by testing different combinations of AR, I, and MA components of the model. The irregular component of time series is what is left after removing the trend and first and second seasonality.

Used extensions & nodes

Created with KNIME Analytics Platform version 4.2.1
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    KNIME Base nodes Trusted extension

    KNIME AG, Zurich, Switzerland

    Version 4.2.2

    knime
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    KNIME Data Generation Trusted extension

    KNIME AG, Zurich, Switzerland

    Version 4.2.0

    knime
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    KNIME Expressions Trusted extension

    KNIME AG, Zurich, Switzerland

    Version 4.2.1

    knime
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    KNIME JavaScript Views Trusted extension

    KNIME AG, Zurich, Switzerland

    Version 4.2.1

    knime
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    KNIME Javasnippet Trusted extension

    KNIME AG, Zurich, Switzerland

    Version 4.2.0

    knime
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    KNIME Math Expression (JEP) Trusted extension

    KNIME AG, Zurich, Switzerland

    Version 4.2.0

    knime
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    KNIME Python Integration Trusted extension

    KNIME AG, Zurich, Switzerland

    Version 4.2.0

    knime
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    KNIME Quick Forms Trusted extension

    KNIME AG, Zurich, Switzerland

    Version 4.2.1

    knime
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    KNIME Statistics Nodes Trusted extension

    KNIME AG, Zurich, Switzerland

    Version 4.2.0

    knime
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