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

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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.

URL: Time Series Analysis with Components https://www.knime.com/blog/time-series-analysis-with-components
URL: Building a Time Series Application https://www.knime.com/blog/building-a-time-series-analysis-application
URL: Scoring Metrics eBook https://www.knime.com/knimepress/scoring-metrics-evaluating-machine-learning-models

Dynamic forecasting and reconstructing the signal to forecasts generated on Sample-Superstore.xls data available on Tableau Community page

Access seasonality lag and trend model

Forecast

Restore seasonality and trend

Assess forecast accuracy

Yearly Seasonality
Restore Seasonality
Training data
Table Reader
Table Row To Variable
Real data for scoring
Table Reader
Joiner
Trend model
PMML Reader
ARIMA
Loop Deployment
Line Plot
RowID
seed data
Table Reader
Numeric Scorer
Seasonalities
Table Reader
Trend
Restore Trend

Nodes

Extensions

Links

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: 2025-03-21
Last update: 2026-11-14
Created with KNIME version: v5.1.0
Tags: dynamic deploymentrecursive loopARIMArestore seasonalityrestore trend

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