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03b_​Time_​Series_​AR_​Testing

Anomaly Detection. Time Series AR Testing
This workflow tests the performance of previously trained auto-regressive models for anomaly detection Model deployment and calculating 1st level alarms Calculating and visualizing 2nd level alarms [500-600]+Amp -> AmpLag = 10loop on all columnsi.e. on all frequency bins for each sensorSep 2007 - Jul 21, 2008is the maintenance windowcumulative second level alarmgeneration alarm level 1for all time seriesmoving averageon level 1 alarmsand alarm level 2generation2 level alarms forone sensor (SV3)read errorstatsread AlignedData.csvadd original datetime columnColumn Rename Lag Column Column ListLoop Start Loop End (ColumnAppend) Missing Value extract Sep 2007- Jul 21, 2008 Prepare for chart RegressionPredictor Stats and Models Stacked Area Chart Alarm level 1 Alarm Level 2 Line Plot (Plotly) Table Reader CSV Reader Joiner This workflow tests the performance of previously trained auto-regressive models for anomaly detection Model deployment and calculating 1st level alarms Calculating and visualizing 2nd level alarms [500-600]+Amp -> AmpLag = 10loop on all columnsi.e. on all frequency bins for each sensorSep 2007 - Jul 21, 2008is the maintenance windowcumulative second level alarmgeneration alarm level 1for all time seriesmoving averageon level 1 alarmsand alarm level 2generation2 level alarms forone sensor (SV3)read errorstatsread AlignedData.csvadd original datetime columnColumn Rename Lag Column Column ListLoop Start Loop End (ColumnAppend) Missing Value extract Sep 2007- Jul 21, 2008 Prepare for chart RegressionPredictor Stats and Models Stacked Area Chart Alarm level 1 Alarm Level 2 Line Plot (Plotly) Table Reader CSV Reader Joiner

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On NodePit since: 2019-07-12
Last update: 2024-04-16
Created with KNIME version: v4.5.1
Tags: anomaly detectiontime series analysisauto-regressive modelsIoTInternet of Thingspredictive maintenancesensormanufacturingPracticing Data Science

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