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02_​Time_​Series_​AR_​Training_​dataviz

Anomaly Detection. Time Series AR Training Dataviz
This workflow trains an auto-regressive model for anomaly detection and compares theamplitude values on two frequency bands Model training train a Linear Regression Model for each timeseries columnex:[500-600]+Amp -> AmpLag = 1010 samplesas past historyloop on all 313 columnsi.e. on all frequency binson all sensorsusing Last Value for missing valuesJan-Aug 2007is the training setcalculateerror statsread AlignedData.csvproduced byTime Alignment & Visualization313 time seriessave errorstatsexport statson predictionerrorsLinear RegressionLearner Column Rename Lag Column Column ListLoop Start Missing Value extractJan-Aug 2007 Line Plot Table Creator Color Manager Column Filter Save model RegressionPredictor Error Stats CSV Reader Table Writer Loop End This workflow trains an auto-regressive model for anomaly detection and compares theamplitude values on two frequency bands Model training train a Linear Regression Model for each timeseries columnex:[500-600]+Amp -> AmpLag = 1010 samplesas past historyloop on all 313 columnsi.e. on all frequency binson all sensorsusing Last Value for missing valuesJan-Aug 2007is the training setcalculateerror statsread AlignedData.csvproduced byTime Alignment & Visualization313 time seriessave errorstatsexport statson predictionerrorsLinear RegressionLearner Column Rename Lag Column Column ListLoop Start Missing Value extractJan-Aug 2007 Line Plot Table Creator Color Manager Column Filter Save model RegressionPredictor Error Stats CSV Reader Table Writer Loop End

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On NodePit since: 2019-07-12
Last update: 2024-04-18
Created with KNIME version: v4.6.1
Tags: anomaly detectiontime series analysisauto-regressive modelsIoTInternet of Thingspredictive maintenancesensormanufacturing

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