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Forecasting and Reconstructing Time Series

Dynamic deployment Recursive loop ARIMA Restore seasonality Restore trend

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This workflow forecasts the monthly average sales in 2017 based on monthly average sales between 2014 and 2016 using dynamic deployment. The forecasting model is an ARIMA (0,1,4) model. The forecasted sales values consist of the forecasted residuals and restored seasonality and trend components.

Used extensions & nodes

Created with KNIME Analytics Platform version 4.5.0
  • KNIME Base nodes Trusted extension

    KNIME AG, Zurich, Switzerland

    Version 4.5.0

    knime
  • KNIME Data Generation Trusted extension

    KNIME AG, Zurich, Switzerland

    Version 4.5.0

    knime
  • KNIME JavaScript Views Trusted extension

    KNIME AG, Zurich, Switzerland

    Version 4.5.0

    knime
  • KNIME Javasnippet Trusted extension

    KNIME AG, Zurich, Switzerland

    Version 4.5.0

    knime
  • KNIME Math Expression (JEP) Trusted extension

    KNIME AG, Zurich, Switzerland

    Version 4.5.0

    knime
  • KNIME Python Integration Trusted extension

    KNIME AG, Zurich, Switzerland

    Version 4.5.0

    knime
  • KNIME Quick Forms Trusted extension

    KNIME AG, Zurich, Switzerland

    Version 4.5.0

    knime
  • KNIME Statistics Nodes Trusted extension

    KNIME AG, Zurich, Switzerland

    Version 4.5.0

    knime
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