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03a_​Time_​Series_​AR_​Deployment

Anomaly Detection. Time Series AR Deployment
This workflow deploys a previously trained auto-regressive model for anomaly detection. The input data must include at least 2 months of pastvalues. This workflow calls a separate "Send_email_to _start_checkup" workflow in case of a 2nd level alarm. Model deployment and calculating 1st level alarms Calculating 2nd level alarms and triggering action [500-600]+Amp -> AmpLag = 10loop on all columnsi.e. on all frequency binsfor each sensormoving averageon level 1 alarmsand alarm level 2generationSelect a day > Extract 2 months of its pastCall Workflownodegeneration alarm level 1for all time seriesread errorstatsread AlignedData.csvadd original datetime column Column Rename Lag Column Column ListLoop Start Loop End (ColumnAppend) Missing Value Alarm Level 2 Select Day Trigger check up iflevel 2 Alarm =1 Alarm level 1 RegressionPredictor Stats and Models Table Reader CSV Reader Joiner This workflow deploys a previously trained auto-regressive model for anomaly detection. The input data must include at least 2 months of pastvalues. This workflow calls a separate "Send_email_to _start_checkup" workflow in case of a 2nd level alarm. Model deployment and calculating 1st level alarms Calculating 2nd level alarms and triggering action [500-600]+Amp -> AmpLag = 10loop on all columnsi.e. on all frequency binsfor each sensormoving averageon level 1 alarmsand alarm level 2generationSelect a day > Extract 2 months of its pastCall Workflownodegeneration alarm level 1for all time seriesread errorstatsread AlignedData.csvadd original datetime columnColumn Rename Lag Column Column ListLoop Start Loop End (ColumnAppend) Missing Value Alarm Level 2 Select Day Trigger check up iflevel 2 Alarm =1 Alarm level 1 RegressionPredictor Stats and Models Table Reader CSV Reader Joiner

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