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02_Forecasting_and_Reconstructing_Time_Series

Dynamic deploymentRecursive loopARIMARestore seasonalityRestore trend
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Versionv1.0Latest, created on 
Oct 20, 2023 2:07 PM
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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.

External resources

  • Time Series Analysis with Components
  • Building a Time Series Application
  • Scoring Metrics eBook
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Created with KNIME Analytics Platform version 5.1.0
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