ExistenceRatioThreshold

Description

'Existence Ratio Threshold' verses configure Mona to find segments with a metric's existence ratio (the rate of contexts in which the metric's value is not empty, divided by the total amount of contexts in the segment) exceeding the given hard threshold (either over or under).

Anomaly Level in this verse is the difference between the existence ratio of a segment's metric to the value of the threshold. In case the baseline is over the threshold, the returned value is the difference between its value and the threshold.

{
  "stanzas": {
    "stanza_name": {
      "verses": [
        {
          "type": "ExistenceRatioThreshold",
          "segment_by": [
            "company_id",
            "country"
          ],
          "metrics": [
            "confidence_score",
            "failed_classification"
          ],
          "min_segment_size": 100,
          "threshold": 0.75,
          "time_period": "1d"
        }
      ]
    }
  }
}

In this example we see an ExistenceRatioThreshold verse which is configured to search for any specific countries or companies (or any intersection of country and company), which have a minimum of 100 records, where in the last day (time_period) the existence ratio of "confidence_score" or "failed_classification" is higher then 0.75, which is the threshold.

Basic Params

cadence

Name	Description	Type	Default
cadence	The cadence for evaluation of this verse. Only the following cadences are valid: Minutes: 1m, 5m, 10m, 15m, 20m, 30m. Hours: 1h, 2h, 3h, 4h, 6h, 8h, 12h. Days: 1d, 2d, 3d, 4d, 5d, 6d. Weeks: 1w, 2w, 3w, 4w, 5w.	Cadence	1d

{ "cadence": "6h" }

default_urgency

Name	Description	Type	Default
default_urgency	The urgency class for insights created using this verse. Currently, supports two values: "normal" (default) and "high". If set to "normal", then specific thresholds for "high" urgency can be set using other parameters prefixed with "highurgency". If set to "high", then threshold parameters prefixed with "highurgency" are not considered at all - since all insights of this verse will be considered as having a "high" urgency.	Urgency	normal

{ "default_urgency": "high" }

description

Name	Description	Type	Default
description	Verse description.	str

{ "description": "searches for asc drifts in output_score" }

metrics

Name	Description	Type	Default
metrics	Relevant metrics to search anomalies for in the verse, relevant only for types who search for anomalies in metrics behavior.	MetricsList	()

{ "metrics": [ "top_score", "delta_top_to_second_score" ] }

min_segment_size

Name	Description	Type	Default
min_segment_size	Minimal segment size that's used for the bias check which a segment must have in order to be considered as an outlier.	PositiveInt	100

{ "min_segment_size": 200 }

min_segment_size_fraction

Name	Description	Type	Default
min_segment_size_fraction	Minimal segment size in fraction from baseline segment, which a segment must have in order to be considered in the search.	InclusiveFraction	0

{ "min_segment_size_fraction": 0.05 }

name

Name	Description	Type	Default
name	(Required) The name of the verse. Please note, a verse's name must be different from other verses in the same stanza.	str	None

{ "name": "confidence_outliers" }

over_threshold

Name	Description	Type	Default
over_threshold	If true - means "over" threshold, in which case the metric's value is tested to be larger than the threshold. If false - means "under" threshold, in which case the metric's value is tested to be smaller than the threshold.	bool	True

{ "over_threshold": false }

segment_by

Name	Description	Type	Default
segment_by	The dimensions to use to segment the data in order to search for anomalies. This list must be a sublist of all arc class' dimensions. Limiting the possible values of a specific segmentation field on which insights can be generated can be done using the "avoid_values" and the "include_only_values" keys in the segmentation JSON object, as seen in the example.	SegmentationsList	()

{ "segment_by": [ "city", "bot_id", {"name": "provider-code", "avoid_values": ["zoom"]}, {"name": "selected-language", "avoid_values": ["eng", "spa"]}, {"name": "country", "include_only_values": ["jpn"]} ] }

threshold

Name	Description	Type	Default
threshold	(Required) A hard threshold to compare segment's values with.	float	None

{ "threshold": 5 }

time_period

Name	Description	Type	Default
time_period	Time period in which to search for outliers, ending on the day of the latest available data. Expected format is "" where can be any positive integer, and options currently include: "d" (days), or "w" (weeks). e.g, "1d" means 1 day period	TimePeriodOrEmpty	4w

{ "time_period": "2w" }

Advanced Misc Params

avoid_same_field_for_segment_and_metric

Name	Description	Type	Default
avoid_same_field_for_segment_and_metric	If True, insights would not be created for segments based on the same field as the given metric.	bool	True

{ "avoid_same_field_for_segment_and_metric": false }

cookbook

Name	Description	Type	Default
cookbook	Instructions on how to read an insight generated by this verse. Expected format is MarkDown.	Cookbook

{ "cookbook": "Use **this param** to add instructions using [markdown](https://daringfireball.net/projects/markdown/syntax) syntax on how to read insights generated from this `verse`, and what should the insight recipient do with it." }

create_extra_adjacent_signals

Name	Description	Type	Default
create_extra_adjacent_signals	If set to true (default), will cause Mona to create new signals from existing signals with adjacent numeric segments. So if there are two signals defined on 1 <= x < 2 and 2 <= x < 3 - Mona will automatically create a new signal with 1 <= x < 3. This will allow the Mona clustering algorithm to create an insight with the most relevant segment for its main signal.	bool	True

{ "create_extra_adjacent_signals": false }

disabled

Name	Description	Type	Default
disabled	If set to True - this verse won't be used when searching for new insights.	bool	False

{ "disabled": true }

expire_after

Name	Description	Type	Default
expire_after	Insights detected by this verse will continue to be considered active for at least this amount of time after the last time they were detected.	TimePeriodOrEmpty	3d

{ "expire_after": "2d" }

relevant_data_time_buffer

Name	Description	Type	Default
relevant_data_time_buffer	Adds an end-time buffer to the insight generation. For example - If this param's value is "1d", then insights are generated for a day before the latest received data. This is useful for processes in which it takes a specific period of time to get all the healthy monitoring data in place.	TimePeriodOrEmpty

{ "relevant_data_time_buffer": "1d" }

threshold_inclusive

Name	Description	Type	Default
threshold_inclusive	If true, and a segment's value equals to the hard threshold, it means the criteria for the threshold is fulfilled. If false, equality between a segment's metric value and the threshold means the criteria of the threshold s NOT fulfilled.	bool	True

{ "threshold_inclusive": false }

timestamp_field_name

Name	Description	Type	Default
timestamp_field_name	The field that is used as the time dimension for insight generation.	TimestampField	timestamp

{ "timestamp_field_name": "run_end_time" }

timezone

Name	Description	Type	Default
timezone	The timezone used to aggregate daily data points. Accepts any IANA time zone ID: (https://en.wikipedia.org/wiki/List_of_tz_database_time_zones)	Timezone	UTC

{ "timezone": "Asia/Hong_Kong" }

Advanced Score Calculation Params

score_anomaly_level_exponent

Name	Description	Type	Default
score_anomaly_level_exponent	An exponent to put on the anomaly level in the score after multiplying it by the given multiplier.	float	1

{ "score_anomaly_level_exponent": 0.5 }

score_anomaly_level_multiplier

Name	Description	Type	Default
score_anomaly_level_multiplier	Multiplier for an anomaly level to use before using the exponent.	float	1

{ "score_anomaly_level_multiplier": 1.2 }

score_segment_size_exponent

Name	Description	Type	Default
score_segment_size_exponent	An exponent to put on the segment's size (or relative size) in the combined score. If score_segment_size_log_base is not 0, the exponent will be applied before the logarithm will.	float	0.5

{ "score_segment_size_exponent": 1.5 }

score_segment_size_log_base

Name	Description	Type	Default
score_segment_size_log_base	Changes the log base used for the segment's size (or relative size) in the combined score, or remove the log altogether by setting 0 here. Unless its 0 this value must be larger than 1.	float	0

{ "score_segment_size_log_base": 5 }

score_use_segment_absolute_size

Name	Description	Type	Default
score_use_segment_absolute_size	If true, use the segment absolute size in the combined score, otherwise use the segment's size relative to its baseline (fraction).	bool	True

{ "score_use_segment_absolute_size": false }

Anomaly Thresholds Params

high_urgency_min_score

Name	Description	Type	Default
high_urgency_min_score	Threshold for separating between "high" and "normal" urgency insights with regards to min_score. See "min_score" param for more details on the functionality of this param.	FloatOrNone	None

{ "high_urgency_min_score": 20 }

high_urgency_threshold

Name	Description	Type	Default
high_urgency_threshold	Threshold for separating between "high" and "normal" urgency insights with regards to threshold. See "threshold" param for more details on the functionality of this param.	FloatOrNone	None

{ "high_urgency_threshold": 5 }

min_score

Name	Description	Type	Default
min_score	The minimal score for a signal to be considered as an anomaly.	float	0

{ "min_score": 4 }

over_threshold

Name	Description	Type	Default
over_threshold	If true - means "over" threshold, in which case the metric's value is tested to be larger than the threshold. If false - means "under" threshold, in which case the metric's value is tested to be smaller than the threshold.	bool	True

{ "over_threshold": false }

threshold

Name	Description	Type	Default
threshold	(Required) A hard threshold to compare segment's values with.	float	None

{ "threshold": 5 }

threshold_inclusive

Name	Description	Type	Default
threshold_inclusive	If true, and a segment's value equals to the hard threshold, it means the criteria for the threshold is fulfilled. If false, equality between a segment's metric value and the threshold means the criteria of the threshold s NOT fulfilled.	bool	True

{ "threshold_inclusive": false }

Data Filtering Params

avoid_segmenting_on_missing

Name	Description	Type	Default
avoid_segmenting_on_missing	When true, insights will not be generated for segments which are (partially or fully) defined by a missing field.	bool	False

{ "avoid_segmenting_on_missing": true }

baseline_segment

Name	Description	Type	Default
baseline_segment	The baseline segment of this verse. This segment defines "the world" as far as this verse is concerned. Only data from this segment will be considered when finding insights.	Segment	{}

{ "baseline_segment": { "model_version": [ { "value": "V1" } ] } }

enhance_exclude_segments

Name	Description	Type	Default
enhance_exclude_segments	If True, when exclude segments are added to any level of configuration (either in the verse, the stanza or the stanzas_global_defaults) they are ADDED to the excluded segments of higher level defaults, if exists any. For example, if we have in stanzas_global_default a single excluded segment of {dimensionA: MISSING}, and the stanza (or verse) has a single excluded segment of {dimensionB: 0}, then if enhance_exclude_segments is True, the excluded segments will include both {dimensionA: MISSING} and {dimensionB: 0} and will filter either one. Otherwise (if enhance_exclude_segments is False) it will be overridden to just the one segment in the verse {dimensionB: 0}.	bool	False

{ "enhance_exclude_segments": true }

exclude_segments

Name	Description	Type	Default
exclude_segments	Segments to exclude in the baseline of this verse. Each data we search for will not include these segments - both tested segments as well as any benchmarks used to find the anomalies. Notice that whether or not this param will override definitions for exclude_segments in other levels is decided by enhance_exclude_segments.	SegmentsList	()

{ "exclude_segments": [ { "text_length": [ { "min_value": 0, "max_value": 100 } ] } ] }

Related Anomalies Params

avoid_related_anomalies_for

Name	Description	Type	Default
avoid_related_anomalies_for	A list of fields to avoid checking for correlated anomalies to the main anomaly in a generated insight. See "find_related_anomalies_for" for further details.	MetricsList	()

{ "avoid_related_anomalies_for": ["delta_top_to_second_score"] }

find_related_anomalies_for

Name	Description	Type	Default
find_related_anomalies_for	A list of fields to check for correlated anomalies to the main anomaly in a generated insight. These correlated anomalies might help with understanding the possible cause of an insight. Leave empty to search in all fields.	MetricsList	()

{ "find_related_anomalies_for": ["sentiment_score", "confidence_interval"] }

related_anomalies_min_correlation

Name	Description	Type	Default
related_anomalies_min_correlation	Minimal Pearson correlation between the metric on which an anomaly was found and another metric with an anomaly on the same segment, below which Mona will not use the other metric as a related anomaly.	NonNegativeFloat	0.3

{ "related_anomalies_min_correlation": 0.5 }

Required Params

name

Name	Description	Type	Default
name	(Required) The name of the verse. Please note, a verse's name must be different from other verses in the same stanza.	str	None

{ "name": "confidence_outliers" }

threshold

Name	Description	Type	Default
threshold	(Required) A hard threshold to compare segment's values with.	float	None

{ "threshold": 5 }

Segmentation Params

always_segment_baseline_by

Name	Description	Type	Default
always_segment_baseline_by	A list of dimensions to always segment the baseline segment by. This is useful when separating the world to completely unrelated parts - e.g., in a case where you have a different model developed for each customer and there's no need to look for insights across different customers. Limiting the possible values of a specific segmentation field on which insights can be generated can be done using the "avoid_values" and the "include_only_values" keys in the segmentation JSON object, as seen in the example.	SegmentationsList	()

{ "always_segment_baseline_by": [ "country", {"name": "city", "avoid_values": ["Tel Aviv"]}, ] }

avoid_segmenting_on_missing

Name	Description	Type	Default
avoid_segmenting_on_missing	When true, insights will not be generated for segments which are (partially or fully) defined by a missing field.	bool	False

{ "avoid_segmenting_on_missing": true }

max_segment_baseline_by_depth

Name	Description	Type	Default
max_segment_baseline_by_depth	The maximum number of fields Mona should combine for segmenting the baseline (if "segment_baseline_by" fields given).	PositiveInt	2

{ "max_segment_baseline_by_depth": 3 }

max_segment_by_depth

Name	Description	Type	Default
max_segment_by_depth	The maximum number of fields Mona should combine to create sub-segments to search in. Baseline segment fields and parent fields are "free", and are not counted for depth. Notice this parameter has a exponential effect on the performance and should be kept within SLAs.	PositiveInt	2

{ "max_segment_by_depth": 3 }

min_segment_baseline_by_depth

Name	Description	Type	Default
min_segment_baseline_by_depth	The minimum number of fields Mona should combine for segmenting the baseline (if "segment_baseline_by" fields given).	NonNegativeInt	0

{ "min_segment_baseline_by_depth": 1 }

min_segment_by_depth

Name	Description	Type	Default
min_segment_by_depth	The minimum number of fields Mona should combine to create sub-segments to search in.	NonNegativeInt	0

{ "min_segment_by_depth": 1 }

segment_baseline_by

Name	Description	Type	Default
segment_baseline_by	A list of dimensions to potentially segment the baseline segment by. Limiting the possible values of a specific segmentation field on which insights can be generated can be done using the "avoid_values" and the "include_only_values" keys in the segmentation JSON object.	SegmentationsList	()

{ "segment_baseline_by": [ "model_version" ] }

segment_by

Name	Description	Type	Default
segment_by	The dimensions to use to segment the data in order to search for anomalies. This list must be a sublist of all arc class' dimensions. Limiting the possible values of a specific segmentation field on which insights can be generated can be done using the "avoid_values" and the "include_only_values" keys in the segmentation JSON object, as seen in the example.	SegmentationsList	()

{ "segment_by": [ "city", "bot_id", {"name": "provider-code", "avoid_values": ["zoom"]}, {"name": "selected-language", "avoid_values": ["eng", "spa"]}, {"name": "country", "include_only_values": ["jpn"]} ] }

Size Thresholds Params

baseline_min_segment_size

Name	Description	Type	Default
baseline_min_segment_size	Minimal segment size for the baseline segment.	PositiveFloat	1

{ "baseline_min_segment_size": 100 }

high_urgency_baseline_min_segment_size

Name	Description	Type	Default
high_urgency_baseline_min_segment_size	Threshold for separating between "high" and "normal" urgency insights with regards to baseline_min_segment_size. See "baseline_min_segment_size" param for more details on the functionality of this param.	PositiveFloatOrNone	None

{ "high_urgency_baseline_min_segment_size": 1000 }

high_urgency_min_segment_size

Name	Description	Type	Default
high_urgency_min_segment_size	Threshold for separating between "high" and "normal" urgency insights with regards to min_segment_size. See "min_segment_size" param for more details on the functionality of this param.	PositiveIntOrNone	None

{ "high_urgency_min_segment_size": 1000 }

high_urgency_min_segment_size_fraction

Name	Description	Type	Default
high_urgency_min_segment_size_fraction	Threshold for separating between "high" and "normal" urgency insights with regards to min_segment_size_fraction. See "min_segment_size_fraction" param for more details on the functionality of this param.	InclusiveFractionOrNone	None

{ "high_urgency_min_segment_size_fraction": 0.2 }

max_segment_size

Name	Description	Type	Default
max_segment_size	Maximal segment size which a segment must have (bigger segments won't be considered in the search). Leave empty to not have such a threshold.	PositiveIntOrNone	None

{ "max_segment_size": 10000 }

max_segment_size_fraction

Name	Description	Type	Default
max_segment_size_fraction	Maximal segment size in fraction from baseline segment, which a segment must have. Leave empty to not have such a threshold.	NonInclusiveFractionOrNone	None

{ "max_segment_size_fraction": 0.2 }

min_segment_size

Name	Description	Type	Default
min_segment_size	Minimal segment size that's used for the bias check which a segment must have in order to be considered as an outlier.	PositiveInt	100

{ "min_segment_size": 200 }

min_segment_size_fraction

Name	Description	Type	Default
min_segment_size_fraction	Minimal segment size in fraction from baseline segment, which a segment must have in order to be considered in the search.	InclusiveFraction	0

{ "min_segment_size_fraction": 0.05 }

Time Related Params

cadence

Name	Description	Type	Default
cadence	The cadence for evaluation of this verse. Only the following cadences are valid: Minutes: 1m, 5m, 10m, 15m, 20m, 30m. Hours: 1h, 2h, 3h, 4h, 6h, 8h, 12h. Days: 1d, 2d, 3d, 4d, 5d, 6d. Weeks: 1w, 2w, 3w, 4w, 5w.	Cadence	1d

{ "cadence": "6h" }

expire_after

Name	Description	Type	Default
expire_after	Insights detected by this verse will continue to be considered active for at least this amount of time after the last time they were detected.	TimePeriodOrEmpty	3d

{ "expire_after": "2d" }

relevant_data_time_buffer

Name	Description	Type	Default
relevant_data_time_buffer	Adds an end-time buffer to the insight generation. For example - If this param's value is "1d", then insights are generated for a day before the latest received data. This is useful for processes in which it takes a specific period of time to get all the healthy monitoring data in place.	TimePeriodOrEmpty

{ "relevant_data_time_buffer": "1d" }

time_period

Name	Description	Type	Default
time_period	Time period in which to search for outliers, ending on the day of the latest available data. Expected format is "" where can be any positive integer, and options currently include: "d" (days), or "w" (weeks). e.g, "1d" means 1 day period	TimePeriodOrEmpty	4w

{ "time_period": "2w" }

timestamp_field_name

Name	Description	Type	Default
timestamp_field_name	The field that is used as the time dimension for insight generation.	TimestampField	timestamp

{ "timestamp_field_name": "run_end_time" }

timezone

Name	Description	Type	Default
timezone	The timezone used to aggregate daily data points. Accepts any IANA time zone ID: (https://en.wikipedia.org/wiki/List_of_tz_database_time_zones)	Timezone	UTC

{ "timezone": "Asia/Hong_Kong" }

Urgency Params

default_urgency

Name	Description	Type	Default
default_urgency	The urgency class for insights created using this verse. Currently, supports two values: "normal" (default) and "high". If set to "normal", then specific thresholds for "high" urgency can be set using other parameters prefixed with "highurgency". If set to "high", then threshold parameters prefixed with "highurgency" are not considered at all - since all insights of this verse will be considered as having a "high" urgency.	Urgency	normal

{ "default_urgency": "high" }

high_urgency_baseline_min_segment_size

Name	Description	Type	Default
high_urgency_baseline_min_segment_size	Threshold for separating between "high" and "normal" urgency insights with regards to baseline_min_segment_size. See "baseline_min_segment_size" param for more details on the functionality of this param.	PositiveFloatOrNone	None

{ "high_urgency_baseline_min_segment_size": 1000 }

high_urgency_min_score

Name	Description	Type	Default
high_urgency_min_score	Threshold for separating between "high" and "normal" urgency insights with regards to min_score. See "min_score" param for more details on the functionality of this param.	FloatOrNone	None

{ "high_urgency_min_score": 20 }

high_urgency_min_segment_size

Name	Description	Type	Default
high_urgency_min_segment_size	Threshold for separating between "high" and "normal" urgency insights with regards to min_segment_size. See "min_segment_size" param for more details on the functionality of this param.	PositiveIntOrNone	None

{ "high_urgency_min_segment_size": 1000 }

high_urgency_min_segment_size_fraction

Name	Description	Type	Default
high_urgency_min_segment_size_fraction	Threshold for separating between "high" and "normal" urgency insights with regards to min_segment_size_fraction. See "min_segment_size_fraction" param for more details on the functionality of this param.	InclusiveFractionOrNone	None

{ "high_urgency_min_segment_size_fraction": 0.2 }

high_urgency_require_all_criteria

Name	Description	Type	Default
high_urgency_require_all_criteria	Decide if to use 'AND'/'OR' condition between all high_urgency threshold params.	bool	True

{ "high_urgency_require_all_criteria": false }

high_urgency_threshold

Name	Description	Type	Default
high_urgency_threshold	Threshold for separating between "high" and "normal" urgency insights with regards to threshold. See "threshold" param for more details on the functionality of this param.	FloatOrNone	None

{ "high_urgency_threshold": 5 }

Visuals and Enrichments Params

field_vectors

Name	Description	Type	Default
field_vectors	This attribute lists metric vectors for the FE to show on an insight card of this verse. A value in this field can either be a string (in which case the string should correspond to a kapi_vector name in the config) or an array (in which case the array should be treated as an ad-hoc kapi vector defined specifically for this verse).	FieldVectorsList	()

{ "field_vectors": [ "field_vector_group_1", "field_vector_group_2", "field_vector_group_3" ] }

investigate_no_drill

Name	Description	Type	Default
investigate_no_drill	Dictates the link to the investigations page to add to the found insights. If True, the link will point to investigations page with a drilldown to the segment that was found. If it's false the link will point to the investigations page without drilldown, but with the found segment selected, so it can be compared with a benchmark of a higher level.	bool	False

{ "investigate_no_drill": true }

Wizard Params

cadence

Name	Description	Type	Default
cadence	The cadence for evaluation of this verse. Only the following cadences are valid: Minutes: 1m, 5m, 10m, 15m, 20m, 30m. Hours: 1h, 2h, 3h, 4h, 6h, 8h, 12h. Days: 1d, 2d, 3d, 4d, 5d, 6d. Weeks: 1w, 2w, 3w, 4w, 5w.	Cadence	1d

{ "cadence": "6h" }

default_urgency

Name	Description	Type	Default
default_urgency	The urgency class for insights created using this verse. Currently, supports two values: "normal" (default) and "high". If set to "normal", then specific thresholds for "high" urgency can be set using other parameters prefixed with "highurgency". If set to "high", then threshold parameters prefixed with "highurgency" are not considered at all - since all insights of this verse will be considered as having a "high" urgency.	Urgency	normal

{ "default_urgency": "high" }

metrics

Name	Description	Type	Default
metrics	Relevant metrics to search anomalies for in the verse, relevant only for types who search for anomalies in metrics behavior.	MetricsList	()

{ "metrics": [ "top_score", "delta_top_to_second_score" ] }

min_segment_size

Name	Description	Type	Default
min_segment_size	Minimal segment size that's used for the bias check which a segment must have in order to be considered as an outlier.	PositiveInt	100

{ "min_segment_size": 200 }

min_segment_size_fraction

Name	Description	Type	Default
min_segment_size_fraction	Minimal segment size in fraction from baseline segment, which a segment must have in order to be considered in the search.	InclusiveFraction	0

{ "min_segment_size_fraction": 0.05 }

name

Name	Description	Type	Default
name	(Required) The name of the verse. Please note, a verse's name must be different from other verses in the same stanza.	str	None

{ "name": "confidence_outliers" }

over_threshold

Name	Description	Type	Default
over_threshold	If true - means "over" threshold, in which case the metric's value is tested to be larger than the threshold. If false - means "under" threshold, in which case the metric's value is tested to be smaller than the threshold.	bool	True

{ "over_threshold": false }

segment_by

Name	Description	Type	Default
segment_by	The dimensions to use to segment the data in order to search for anomalies. This list must be a sublist of all arc class' dimensions. Limiting the possible values of a specific segmentation field on which insights can be generated can be done using the "avoid_values" and the "include_only_values" keys in the segmentation JSON object, as seen in the example.	SegmentationsList	()

{ "segment_by": [ "city", "bot_id", {"name": "provider-code", "avoid_values": ["zoom"]}, {"name": "selected-language", "avoid_values": ["eng", "spa"]}, {"name": "country", "include_only_values": ["jpn"]} ] }

threshold

Name	Description	Type	Default
threshold	(Required) A hard threshold to compare segment's values with.	float	None

{ "threshold": 5 }

time_period

Name	Description	Type	Default
time_period	Time period in which to search for outliers, ending on the day of the latest available data. Expected format is "" where can be any positive integer, and options currently include: "d" (days), or "w" (weeks). e.g, "1d" means 1 day period	TimePeriodOrEmpty	4w

{ "time_period": "2w" }