startree-matrix-profile-query-dx

–

–

–

–

–

Timezone used to group by time. In TZ-identifier(opens in a new tab) format.

For instance, UTC or US/Pacific.

UTC

AUTO

Required if timeColumn is not AUTO. Learn more(opens in a new tab)

The time for your data to be considered complete and ready for anomaly detection. In ISO-8601 format. Example: PT2H. Learn more(opens in a new tab)

P0D

${queryFilters}

100000001

Historical time period to use to train the model. In ISO-8601 format. Example: P21D.

–

Biggest seasonality period to learn. In ISO-8601 format. Example: P7D.

For advanced users. Type of distance to use for the matrix profile computation. NORMALIZED or NON_NORMALIZED.

NORMALIZED

Anomaly score threshold. If the anomaly score returned by the algorithm is bigger than this threshold, return an anomaly. The smaller, the more anomalies are detected.

1

true

Experimental. Post-processing of the matrix profile anomaly scores. FIRST_ORDER_DIFFERENCE is good at detecting spikes. DIRECT is good at detecting drifts.

FIRST_ORDER_DIFFERENCE

Used to ignore anomalies that happen at specific time periods. A list of days. Anomalies happening on these days are ignored if timeOfWeekIgnore is true. Example: ["MONDAY", "SUNDAY"].

[]

Used to ignore anomalies that happen at specific time periods. A list of hours. Anomalies happening on these hours are ignored. Example: [0,1,2,23]

[]

Used to ignore anomalies that happen at specific time periods. A mapping of {DAY: [hours]}. Anomalies happening on these timeframes are ignored if timeOfWeekIgnore is true. Example: {"FRIDAY": [22, 23], "SATURDAY": [0, 1, 2]}

{}

Used to ignore anomalies that don’t meet the thresholdFilter min and max. Example: set thresholdFilterMin = 10 to ignore anomalies when the metric is smaller than 10. Can help ignore anomalies happening in low data regimes. Filter threshold minimum. If -1, no minimum threshold is applied.

-1

Used to ignore anomalies that don’t meet the thresholdFilter min and max. Example: set thresholdFilterMin = 10 to ignore anomalies when the metric is smaller than 10. Can help ignore anomalies happening in low data regimes. Filter threshold maximum. If -1, no maximum threshold is applied.

-1

Used to ignore anomalies that don’t meet the guardrail threshold. Minimum threshold of the guardrail metric. If -1, no minimum threshold is applied.

-1

Used to ignore anomalies that don’t meet the guardrail threshold. Maximum threshold of guardrailMetric. If -1, no maximum threshold is applied.

-1

Used to ignore anomalies that don’t meet the guardrail threshold. Metric to use as a threshold guardrail. Example: COUNT(*) and set guardrailMetricMin = 100 to ignore anomalies detected when there is less than 100 observations in the period.

COUNT(*)

Used to ignore anomalies that are not detected as anomalies by a simple model. Whether to detect an anomaly if it’s a drop, a spike or any of the two.

UP_OR_DOWN

Used to ignore anomalies that are not detected as anomalies by a simple model. Detection sensitivity. For instance with offsetBaselineFilterIntervalsMethod=PERCENTAGE, set 50 for a 50% percentage change threshold. With offsetBaselineFilterIntervalsMethod=ABSOLUTE, set 200 for a 200 absolute difference threshold between the metric and the baseline.

-1

Used to ignore anomalies that are not detected as anomalies by a simple model. Method to compute intervals. PERCENTAGE or ABSOLUTE.

Used to ignore anomalies that are not detected as anomalies by a simple model. A list of offsets in ISO-8601 format to use as baseline. Eg [P7D, P14D] will compare the current value to the aggregation of the values of the 2 previous weeks.

[‘P7D’]

Used to ignore anomalies that are not detected as anomalies by a simple model. The aggregation function to use to combine historical values. In MEDIAN, AVERAGE, MIN, MAX and any of PCTXXXXX eg PCT05 (5th percentile), PCT95, PCT999 (99.9th percentile).

MEDIAN

Used to ignore anomalies that happen during events. Sql filter to apply on the events. Learn more

Used to ignore anomalies that happen during events. Offset to apply on startTime and endTime to look around the timeframe. In ISO-8601 format. Example: P1D.

P2D

Used to ignore anomalies that happen during events. List of event types to fetch by. Example: ["HOLIDAY", "DEPLOYMENT"]. [] fetches all events. Use ["__NO_EVENTS"] to disable.

[‘__NO_EVENTS’]

Used to ignore anomalies that happen during events. A period in ISO-8601 format that corresponds to a period that is also impacted by the event. Example: if beforeEventMargin is P1D, if event happens on [Dec 24 0:00, Dec 25 0:00[, the label will be applied to anomalies happening on [Dec 23 0:00 and Dec 25 0:00[

P0D

-1

Use if your data is mutable. ThirdEye will maintain the detection results up to date on the mutable period. For instance, if your last 10 days of data is mutable, set P10D. At each cron detection job, the detection results for the last 10 days will be updated.

P0D

For detection replay when data is mutable. If the percentage difference between an existing anomaly and a new anomaly on the same time frame is above this threshold, renotify. Combined with reNotifyAbsoluteThreshold. Both thresholds must pass to be re-notified. If zero, always renotify. If null or negative, never re-notifies.

-1

For detection replay when data is mutable. If the absolute difference between an existing anomaly and a new anomaly on the same time frame is above this threshold, renotify. Combined with reNotifyPercentageThreshold. Both thresholds must pass to be re-notified. If zero, always renotify. If null or negative, never re-notifies.

-1

Maximum gap between 2 anomalies for anomalies to be merged. In ISO-8601 format. Example: PT2H. The default behavior is to merge consecutive anomalies only. To disable anomaly merging entirely, set this value to P0D.

Maximum duration of an anomaly merger. At merge time, if an anomaly merger would get bigger than this limit, the anomalies are not merged. In ISO-8601 format. Example: P7D.

List of the dimensions (columns in the dataset) to use in RCA drill-downs. If not set or empty, all dimensions of the table are used. Learn more(opens in a new tab).

[]

List of dimensions (columns in the dataset) to ignore in RCA drill-downs. If not set or empty, all dimensions of the table are used. rcaExcludedDimensions and rcaIncludedDimensions cannot be used at the same time.

[]

A list of type to filter on for RCA. Only events that match such types will be shown in the RCA related events tab. Learn more(opens in a new tab).

[]

A Sql filter for RCA events. Only events that match the filter will be shown in the RCA related events tab. Learn more(opens in a new tab).

This is a SQL query that will run on the data source and build enumeration items from that queryExample: “SELECT DISTINCT country, device from pageviews LIMIT 100”. In this case, the enumerator will generate one enumeration item for each country/device combination.

–

List of keys to use to identify the enumeration.

The format is the following:

[

“queryFilters”

]

The keys must be present in the params object of each enumeration.

The keys will be used to generate the dimension exploration id.

The id will be used to identify the enumeration in the detection pipeline.

[‘queryFilters’]