SumSuddenChange

Description

Sudden Change verses configure Mona to find timeseries-based anomalies, in which the latest point in a behavioral time-series data acts very differently than the rest of the time series. The latest point will be considered as an anomaly if it is over (or under) a threshold. To calculate if the latest point is over/under this threshold we first measure the distance between the latest point value and some top (for over) or bottom (for under) percentile (a parameter). We then normalize this difference by the difference between the same top/bottom percentile and the median value of the timeseries.

'Sum Sudden Change' verses configure Mona to find segments, in which a metric's sum per time-frame has gone through a "sudden change" (as defined above) in the latest time frame compared to the rest of the metric's sums time series.

{
  "stanzas": {
    "stanza_name": {
      "verses": [
        {
          "type": "SumSuddenChange",
          "segment_by": [
            "company_id",
            "country"
          ],
          "metrics": [
            "confidence_score",
            "failed_classification"
          ],
          "min_culprit_size": 75,
          "min_anomaly_level": 3,
          "time_series_points": 60
        }
      ]
    }
  }
}

In this example we see a SumSuddenChange verse which is configured to search for statistically significant changes in the sum of "confidence_score" or "failed_classification" in different countries or companies (or any intersection of country and company) in the last day, compared to the 59 days prior to that (time_series_points). We use "min_anomaly_level" to define that a sudden change occurs when the difference between the last day's sum and the top/bottom percentile of the entire time series of sums is at least 3 times the difference between the top/bottom percentile (95th by default) of the entire time series and the time series median. The "min_culprit_size" param will filter out segments that on the day of the anomaly (the last day) have less than 75 records.

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. Unlike MetricsListType, this type supports a list of metrics either as their field names or as objects that state the percentile filter to avoid outlier values.	MetricsObjectList	()

{ "metrics": [ "top_score", { "name": "delta_top_to_second_score", "top_percentile_filter": 95, "bottom_percentile_filter": 5 } ] }

min_anomaly_level

Name	Description	Type	Default
min_anomaly_level	This parameter sets the threshold for the minimal anomaly level for which an insight will be generated. Anomaly Level of this verse is the difference between the last point's value to the median value, normalized by the difference between the top_percentile_benchmark and the median.	PositiveFloat	2.5

{ "min_anomaly_level": 2 }

min_segment_size

Name	Description	Type	Default
min_segment_size	Minimal segment size to require for the entire time-series.	PositiveInt	5

{ "min_segment_size": 10 }

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" }

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"]} ] }

time_resolution

Name	Description	Type	Default
time_resolution	Time series time resolution period. 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	TimeResolution	1d

{ "time_resolution": "1w"" }

time_series_points

Name	Description	Type	Default
time_series_points	Size of desired entire time series.	PositiveInt	60

{ "time_series_points": 30 }

trend_directions

Name	Description	Type	Default
trend_directions	A list of allowed anomalies trends directions - either 'asc' for ascending (anomalies in which the found value is LARGER THAN the relevant benchmark), or 'desc' for descending (anomalies in which the found value is SMALLER THAN the relevant benchmark).	TrendDirections	('asc', 'desc')

{ "trend_directions": [ "asc" ] }

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" }

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 }

top_percentile_benchmark

Name	Description	Type	Default
top_percentile_benchmark	Defines the top/bottom percentile to search for in the time-series (after the last point is removed) to serve as a benchmark for what will be defined as a large difference from the median (to that top/bottom direction). For verses with 'desc' in trend_directions the bottom percentile is used as is. For verses with 'asc' in trend_directions the top percentile is (100 - top_percentile_benchmark). For example, if the top fraction percentile is 5 then for bottom threshold 5th percentile is used as benchmark, whereas for top threshold the 95th percentile is used as benchmark.	PositiveFloat	5

{ "top_percentile_benchmark": 10 }

Anomaly Thresholds Params

epsilon

Name	Description	Type	Default
epsilon	Minimal required absolute difference between value and benchmark. Used to account for statistical errors in stable time series.	NonNegativeFloat	0.01

{ "epsilon": 0.5 }

high_urgency_min_anomaly_level

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

{ "high_urgency_min_anomaly_level": 1.5 }

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 }

min_anomaly_level

Name	Description	Type	Default
min_anomaly_level	This parameter sets the threshold for the minimal anomaly level for which an insight will be generated. Anomaly Level of this verse is the difference between the last point's value to the median value, normalized by the difference between the top_percentile_benchmark and the median.	PositiveFloat	2.5

{ "min_anomaly_level": 2 }

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 }

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" }

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_culprit_size

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

{ "high_urgency_min_culprit_size": 500 }

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_culprit_size

Name	Description	Type	Default
min_culprit_size	Minimal absolute size (number of relevant contexts) of the checked point (latest).	NonNegativeFloat	50

{ "min_culprit_size": 50 }

min_exist_freq

Name	Description	Type	Default
min_exist_freq	The minimum fraction of the timeseries frames in which the segment had any data. Why do we need this? Verses might rely on data that only exists in a few days (or any other time resolution). These cases are usually much less stable and more noisy. Usually these cases are not interesting and should be filtered (with this param). Cases that expect that behavior should reduce this value significantly.	InclusiveFraction	0.25

{ "min_exist_freq": 0.5 }

min_point_size

Name	Description	Type	Default
min_point_size	The minimal absolute size (number of relevant contexts) of all the timeseries points except the checked point. Points under this threshold will be ignored when calculating the percentiles of the time series.	NonNegativeInt	1

{ "min_point_size": 20 }

min_segment_size

Name	Description	Type	Default
min_segment_size	Minimal segment size to require for the entire time-series.	PositiveInt	5

{ "min_segment_size": 10 }

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_resolution

Name	Description	Type	Default
time_resolution	Time series time resolution period. 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	TimeResolution	1d

{ "time_resolution": "1w"" }

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_anomaly_level

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

{ "high_urgency_min_anomaly_level": 1.5 }

high_urgency_min_culprit_size

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

{ "high_urgency_min_culprit_size": 500 }

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 }

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 }

time_resolution

Name	Description	Type	Default
time_resolution	Time series time resolution period. 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	TimeResolution	1d

{ "time_resolution": "1w"" }

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. Unlike MetricsListType, this type supports a list of metrics either as their field names or as objects that state the percentile filter to avoid outlier values.	MetricsObjectList	()

{ "metrics": [ "top_score", { "name": "delta_top_to_second_score", "top_percentile_filter": 95, "bottom_percentile_filter": 5 } ] }

min_anomaly_level

Name	Description	Type	Default
min_anomaly_level	This parameter sets the threshold for the minimal anomaly level for which an insight will be generated. Anomaly Level of this verse is the difference between the last point's value to the median value, normalized by the difference between the top_percentile_benchmark and the median.	PositiveFloat	2.5

{ "min_anomaly_level": 2 }

min_segment_size

Name	Description	Type	Default
min_segment_size	Minimal segment size to require for the entire time-series.	PositiveInt	5

{ "min_segment_size": 10 }

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" }

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"]} ] }

time_resolution

Name	Description	Type	Default
time_resolution	Time series time resolution period. 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	TimeResolution	1d

{ "time_resolution": "1w"" }

time_series_points

Name	Description	Type	Default
time_series_points	Size of desired entire time series.	PositiveInt	60

{ "time_series_points": 30 }

trend_directions

Name	Description	Type	Default
trend_directions	A list of allowed anomalies trends directions - either 'asc' for ascending (anomalies in which the found value is LARGER THAN the relevant benchmark), or 'desc' for descending (anomalies in which the found value is SMALLER THAN the relevant benchmark).	TrendDirections	('asc', 'desc')

{ "trend_directions": [ "asc" ] }