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02_​Forecasting_​and_​Reconstructing_​Time_​Series

Forecasting and Reconstructing Time Series

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.


Dynamic forecasting and reconstructing the signal Access seasonality lag and trend model Forecast Restore seasonality and trend Assess forecast accuracy Yearly SeasonalityTrendARIMASeasonalitiesseed dataTraining dataReal data for scoring Trend model Restore Seasonality Line color Restore Trend Line Plot Numeric Scorer RowID Loop Deployment Table Reader Table Reader Table Reader Table Reader Table Rowto Variable PMML Reader Joiner Dynamic forecasting and reconstructing the signal Access seasonality lag and trend model Forecast Restore seasonality and trend Assess forecast accuracy Yearly SeasonalityTrendARIMASeasonalitiesseed dataTraining dataReal data for scoring Trend model Restore Seasonality Line color Restore Trend Line Plot Numeric Scorer RowID Loop Deployment Table Reader Table Reader Table Reader Table Reader Table Rowto Variable PMML Reader Joiner

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Download

To use this workflow in KNIME, download it from the below URL and open it in KNIME:

Download Workflow
Created by: Maarit Laukkanen
Created at: 2020-06-09
On NodePit since: 2023-03-24
Last update: 2024-04-28
Created with KNIME version: v4.4.0
Tags: dynamic deploymentrecursive loopARIMArestore seasonalityrestore trend

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