Anomaly filters enable you to tune your alerts for improved accuracy. Read concepts - using Anomaly filters as part of post-processor for background information. These filters can be defined during the alert creation. See how to create alerts to learn more about alert creation. The goal of anomaly filters is to reduce false alerts.

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Day of the week filter

This filter enables you to define the day of the week that will be used to filter anomalies to reduce false alarms. These days are usually known to be high volume or low volume. 
DaysOfWeek: ["MONDAY", "SATURDAY"],
HoursOfDay: [0,1,2,23],
DayHoursOfWeek: {"FRIDAY": [22, 23], "SATURDAY": [0, 1, 2]}

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Offsets filter

This filter enables you to define an offset to ignore anomalies during alert creation/configuration. 
Metric Name: "Business KPI", # customize label
Offsets: ["P7D", "P14D"], # parameter of the detector
Aggregation: "MEDIAN" # parameter of the detector

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Cold Start filter

Usually model predictions are impacted by low data volume in the beginning. This filter enables you to define from which day/time the model should start learning to predict anomalies accurately. 
coldStartPeriod": "P14D", # filter anomalies in the first 14 days of the dataset
tableName: "my_dataset"

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Events filter

This filter enables you to define specific holiday periods. 
BeforeHolidayMargin: "PT4H",
AfterHolidayMargin: "PT4H"

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Threshold filter

This filter enables you to apply a threshold on the record volume. 
Min limit: "100",
Max limit": "10000000",
Metric Name: "controlMetric",   # customize label
Timestamp: "ts",                # time column of the side input
Threshold metric: "sideMetric"  # column to threshold on in the side input

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Time of the week filter

This filter enables you to specify day of the week and the time window during which the anomalies will be ignored. 
Days Of Week": ["MONDAY", "SATURDAY"],
Hours Of Day": [0,1,2,23],
Day Hours Of Week": {"FRIDAY": [22, 23], "SATURDAY": [0, 1, 2]}