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

Forecasting and Reconstructing Time Series
Dynamic forecasting and reconstructing the signal Access seasonality lag and trend model Forecast Restore seasonality and trend Assess forecast accuracy Yearly SeasonalitySeasonalitiesReal data for scoringTrend modelseed dataTrendTraining dataARIMA Restore Seasonality Table Reader Table Reader PMML Reader Table Reader Line color Restore Trend Table Rowto Variable Table Reader Line Plot Numeric Scorer RowID Joiner Loop Deployment Dynamic forecasting and reconstructing the signal Access seasonality lag and trend model Forecast Restore seasonality and trend Assess forecast accuracy Yearly SeasonalitySeasonalitiesReal data for scoringTrend modelseed dataTrendTraining dataARIMA Restore Seasonality Table Reader Table Reader PMML Reader Table Reader Line color Restore Trend Table Rowto Variable Table Reader Line Plot Numeric Scorer RowID Joiner Loop Deployment

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: 2020-09-15
Last update: 2024-12-21
Created with KNIME version: v4.1.3
Tags: dynamic deploymentrecursive loopARIMArestore seasonalityrestore trend

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