docs-v2/content/flux/v0/stdlib/universe/exponentialmovingaverage.md

title	description	menu	weight	flux/v0/tags	introduced
exponentialMovingAverage() function	`exponentialMovingAverage()` calculates the exponential moving average of `n` number of values in the `_value` column giving more weight to more recent data.	flux_v0_ref name parent identifier exponentialMovingAverage universe universe/exponentialMovingAverage	101	transformations	0.37.0

exponentialMovingAverage() calculates the exponential moving average of n number of values in the _value column giving more weight to more recent data.

Exponential moving average rules

• The first value of an exponential moving average over n values is the algebraic mean of n values.
• Subsequent values are calculated as y(t) = x(t) * k + y(t-1) * (1 - k), where:
  • y(t) is the exponential moving average at time t.
  • x(t) is the value at time t.
  • k = 2 / (1 + n).
• The average over a period populated by only null values is null.
• Exponential moving averages skip null values.

Function type signature

(<-tables: stream[{A with _value: B}], n: int) => stream[{A with _value: B}] where B: Numeric

{{% caption %}} For more information, see Function type signatures. {{% /caption %}}

Parameters

n

({{< req >}}) Number of values to average.

tables

Input data. Default is piped-forward data (<-).

Examples

• Calculate a three point exponential moving average
• Calculate a three point exponential moving average with null values

Calculate a three point exponential moving average

import "sampledata"

sampledata.int()
    |> exponentialMovingAverage(n: 3)

{{< expand-wrapper >}} {{% expand "View example input and output" %}}

Input data

_time	_value	*tag
2021-01-01T00:00:00Z	-2	t1
2021-01-01T00:00:10Z	10	t1
2021-01-01T00:00:20Z	7	t1
2021-01-01T00:00:30Z	17	t1
2021-01-01T00:00:40Z	15	t1
2021-01-01T00:00:50Z	4	t1

_time	_value	*tag
2021-01-01T00:00:00Z	19	t2
2021-01-01T00:00:10Z	4	t2
2021-01-01T00:00:20Z	-3	t2
2021-01-01T00:00:30Z	19	t2
2021-01-01T00:00:40Z	13	t2
2021-01-01T00:00:50Z	1	t2

Output data

_time	_value	*tag
2021-01-01T00:00:20Z	5	t1
2021-01-01T00:00:30Z	11	t1
2021-01-01T00:00:40Z	13	t1
2021-01-01T00:00:50Z	8.5	t1

_time	_value	*tag
2021-01-01T00:00:20Z	6.666666666666667	t2
2021-01-01T00:00:30Z	12.833333333333334	t2
2021-01-01T00:00:40Z	12.916666666666668	t2
2021-01-01T00:00:50Z	6.958333333333334	t2

{{% /expand %}} {{< /expand-wrapper >}}

Calculate a three point exponential moving average with null values

import "sampledata"

sampledata.int(includeNull: true)
    |> exponentialMovingAverage(n: 3)

{{< expand-wrapper >}} {{% expand "View example input and output" %}}

Input data

_time	_value	*tag
2021-01-01T00:00:00Z	-2	t1
2021-01-01T00:00:10Z		t1
2021-01-01T00:00:20Z	7	t1
2021-01-01T00:00:30Z		t1
2021-01-01T00:00:40Z		t1
2021-01-01T00:00:50Z	4	t1

_time	_value	*tag
2021-01-01T00:00:00Z		t2
2021-01-01T00:00:10Z	4	t2
2021-01-01T00:00:20Z	-3	t2
2021-01-01T00:00:30Z	19	t2
2021-01-01T00:00:40Z		t2
2021-01-01T00:00:50Z	1	t2

Output data

_time	_value	*tag
2021-01-01T00:00:20Z	2.5	t1
2021-01-01T00:00:30Z	2.5	t1
2021-01-01T00:00:40Z	2.5	t1
2021-01-01T00:00:50Z	3.25	t1

_time	_value	*tag
2021-01-01T00:00:20Z	0.5	t2
2021-01-01T00:00:30Z	9.75	t2
2021-01-01T00:00:40Z	9.75	t2
2021-01-01T00:00:50Z	5.375	t2

{{% /expand %}} {{< /expand-wrapper >}}