[role="xpack"]
[[search-aggregations-change-point-aggregation]]
=== Change point aggregation
++++
<titleabbrev>Change point</titleabbrev>
++++

experimental::[]

A sibling pipeline that detects, spikes, dips, and change points in a metric. 
Given a distribution of values provided by the sibling multi-bucket aggregation, 
this aggregation indicates the bucket of any spike or dip and/or the bucket at 
which the largest change in the distribution of values, if they are 
statistically significant. 

TIP: It is recommended to use the change point aggregation to detect changes in 
time-based data, however, you can use any metric to create buckets.



[[change-point-agg-syntax]]
==== Parameters

`buckets_path`::
(Required, string)
Path to the buckets that contain one set of values in which to detect a change 
point. There must be at least 22 bucketed values. Fewer than 1,000 is preferred.
For syntax, see <<buckets-path-syntax>>.

==== Syntax

A `change_point` aggregation looks like this in isolation:

[source,js]
--------------------------------------------------
{
  "change_point": {
    "buckets_path": "date_histogram>_count" <1>
  }
}
--------------------------------------------------
// NOTCONSOLE
<1> The buckets containing the values to test against.

[[change-point-agg-response]]
==== Response body

`bucket`::
(Optional, object)
Values of the bucket that indicates the discovered change point. Not returned if 
no change point was found. All the aggregations in the bucket are returned as 
well.
+
.Properties of bucket
[%collapsible%open]
====
`key`:::
(value)
The key of the bucket matched. Could be string or numeric.

`doc_count`:::
(number)
The document count of the bucket.
====

`type`::
(object)
The found change point type and its related values. Possible types:
+
--
* `dip`: a significant dip occurs at this change point
* `distribution_change`: the overall distribution of the values has changed 
significantly
* `non_stationary`: there is no change point, but the values are not from a 
stationary distribution
* `spike`: a significant spike occurs at this point
* `stationary`: no change point found
* `step_change`: the change indicates a statistically significant step up or 
down in value distribution
* `trend_change`: there is an overall trend change occurring at this point
--

==== Example


The following example uses the Kibana sample data logs data set.

[source,js]
--------------------------------------------------
GET kibana_sample_data_logs/_search
{
  "aggs": {
    "date":{ <1>
      "date_histogram": {
        "field": "@timestamp",
        "fixed_interval": "1d"
      },
      "aggs": {
        "avg": { <2>
          "avg": {
            "field": "bytes"
          }
        }
      }
    },
    "change_points_avg": { <3>
      "change_point": {
        "buckets_path": "date>avg" <4>
      }
    }
  }
}
--------------------------------------------------
// NOTCONSOLE
<1> A date histogram aggregation that creates buckets with one day long 
interval.
<2> A sibling aggregation of the `date` aggregation that calculates the average 
value of the `bytes` field within every bucket.
<3> The change point detection aggregation configuration object.
<4> The path of the aggregation values to detect change points. In this case, 
the input of the change point aggregation is the value of `avg` which is a 
sibling aggregation of `date`.


The request returns a response that is similar to the following: 

[source,js]
--------------------------------------------------
    "change_points_avg" : {
      "bucket" : {
        "key" : "2023-04-29T00:00:00.000Z", <1>
        "doc_count" : 329, <2>
        "avg" : { <3>
          "value" : 4737.209726443769
        }
      },
      "type" : { <4>
        "dip" : {
          "p_value" : 3.8999455212466465e-10, <5>
          "change_point" : 41 <6>
        }
      }
    }
--------------------------------------------------
// NOTCONSOLE
<1> The bucket key that is the change point.
<2> The number of documents in that bucket.
<3> Aggregated values in the bucket.
<4> Type of change found.
<5> The `p_value` indicates how extreme the change is; lower values indicate 
greater change.
<6> The specific bucket where the change occurs (indexing starts at `0`).