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Moving Function Aggregation
editMoving Function Aggregation
editGiven an ordered series of data, the Moving Function aggregation will slide a window across the data and allow the user to specify a custom script that is executed on each window of data. For convenience, a number of common functions are predefined such as min/max, moving averages, etc.
This is conceptually very similar to the Moving Average pipeline aggregation, except it provides more functionality.
Syntax
editA moving_fn
aggregation looks like this in isolation:
{ "moving_fn": { "buckets_path": "the_sum", "window": 10, "script": "MovingFunctions.min(values)" } }
Table 15. moving_avg
Parameters
Parameter Name | Description | Required | Default Value |
---|---|---|---|
|
Path to the metric of interest (see |
Required |
|
|
The size of window to "slide" across the histogram. |
Required |
|
|
The script that should be executed on each window of data |
Required |
moving_fn
aggregations must be embedded inside of a histogram
or date_histogram
aggregation. They can be
embedded like any other metric aggregation:
POST /_search { "size": 0, "aggs": { "my_date_histo":{ "date_histogram":{ "field":"date", "calendar_interval":"1M" }, "aggs":{ "the_sum":{ "sum":{ "field": "price" } }, "the_movfn": { "moving_fn": { "buckets_path": "the_sum", "window": 10, "script": "MovingFunctions.unweightedAvg(values)" } } } } } }
A |
|
A |
|
Finally, we specify a |
Moving averages are built by first specifying a histogram
or date_histogram
over a field. You can then optionally
add numeric metrics, such as a sum
, inside of that histogram. Finally, the moving_fn
is embedded inside the histogram.
The buckets_path
parameter is then used to "point" at one of the sibling metrics inside of the histogram (see
buckets_path
Syntax for a description of the syntax for buckets_path
.
An example response from the above aggregation may look like:
{ "took": 11, "timed_out": false, "_shards": ..., "hits": ..., "aggregations": { "my_date_histo": { "buckets": [ { "key_as_string": "2015/01/01 00:00:00", "key": 1420070400000, "doc_count": 3, "the_sum": { "value": 550.0 }, "the_movfn": { "value": null } }, { "key_as_string": "2015/02/01 00:00:00", "key": 1422748800000, "doc_count": 2, "the_sum": { "value": 60.0 }, "the_movfn": { "value": 550.0 } }, { "key_as_string": "2015/03/01 00:00:00", "key": 1425168000000, "doc_count": 2, "the_sum": { "value": 375.0 }, "the_movfn": { "value": 305.0 } } ] } } }
Custom user scripting
editThe Moving Function aggregation allows the user to specify any arbitrary script to define custom logic. The script is invoked each time a
new window of data is collected. These values are provided to the script in the values
variable. The script should then perform some
kind of calculation and emit a single double
as the result. Emitting null
is not permitted, although NaN
and +/- Inf
are allowed.
For example, this script will simply return the first value from the window, or NaN
if no values are available:
POST /_search { "size": 0, "aggs": { "my_date_histo":{ "date_histogram":{ "field":"date", "calendar_interval":"1M" }, "aggs":{ "the_sum":{ "sum":{ "field": "price" } }, "the_movavg": { "moving_fn": { "buckets_path": "the_sum", "window": 10, "script": "return values.length > 0 ? values[0] : Double.NaN" } } } } } }
Pre-built Functions
editFor convenience, a number of functions have been prebuilt and are available inside the moving_fn
script context:
-
max()
-
min()
-
sum()
-
stdDev()
-
unweightedAvg()
-
linearWeightedAvg()
-
ewma()
-
holt()
-
holtWinters()
The functions are available from the MovingFunctions
namespace. E.g. MovingFunctions.max()
max Function
editThis function accepts a collection of doubles and returns the maximum value in that window. null
and NaN
values are ignored; the maximum
is only calculated over the real values. If the window is empty, or all values are null
/NaN
, NaN
is returned as the result.
Table 16. max(double[] values)
Parameters
Parameter Name | Description |
---|---|
|
The window of values to find the maximum |
POST /_search { "size": 0, "aggs": { "my_date_histo":{ "date_histogram":{ "field":"date", "calendar_interval":"1M" }, "aggs":{ "the_sum":{ "sum":{ "field": "price" } }, "the_moving_max": { "moving_fn": { "buckets_path": "the_sum", "window": 10, "script": "MovingFunctions.max(values)" } } } } } }
min Function
editThis function accepts a collection of doubles and returns the minimum value in that window. null
and NaN
values are ignored; the minimum
is only calculated over the real values. If the window is empty, or all values are null
/NaN
, NaN
is returned as the result.
Table 17. min(double[] values)
Parameters
Parameter Name | Description |
---|---|
|
The window of values to find the minimum |
POST /_search { "size": 0, "aggs": { "my_date_histo":{ "date_histogram":{ "field":"date", "calendar_interval":"1M" }, "aggs":{ "the_sum":{ "sum":{ "field": "price" } }, "the_moving_min": { "moving_fn": { "buckets_path": "the_sum", "window": 10, "script": "MovingFunctions.min(values)" } } } } } }
sum Function
editThis function accepts a collection of doubles and returns the sum of the values in that window. null
and NaN
values are ignored;
the sum is only calculated over the real values. If the window is empty, or all values are null
/NaN
, 0.0
is returned as the result.
Table 18. sum(double[] values)
Parameters
Parameter Name | Description |
---|---|
|
The window of values to find the sum of |
POST /_search { "size": 0, "aggs": { "my_date_histo":{ "date_histogram":{ "field":"date", "calendar_interval":"1M" }, "aggs":{ "the_sum":{ "sum":{ "field": "price" } }, "the_moving_sum": { "moving_fn": { "buckets_path": "the_sum", "window": 10, "script": "MovingFunctions.sum(values)" } } } } } }
stdDev Function
editThis function accepts a collection of doubles and average, then returns the standard deviation of the values in that window.
null
and NaN
values are ignored; the sum is only calculated over the real values. If the window is empty, or all values are
null
/NaN
, 0.0
is returned as the result.
Table 19. stdDev(double[] values)
Parameters
Parameter Name | Description |
---|---|
|
The window of values to find the standard deviation of |
|
The average of the window |
POST /_search { "size": 0, "aggs": { "my_date_histo":{ "date_histogram":{ "field":"date", "calendar_interval":"1M" }, "aggs":{ "the_sum":{ "sum":{ "field": "price" } }, "the_moving_sum": { "moving_fn": { "buckets_path": "the_sum", "window": 10, "script": "MovingFunctions.stdDev(values, MovingFunctions.unweightedAvg(values))" } } } } } }
The avg
parameter must be provided to the standard deviation function because different styles of averages can be computed on the window
(simple, linearly weighted, etc). The various moving averages that are detailed below can be used to calculate the average for the
standard deviation function.
unweightedAvg Function
editThe unweightedAvg
function calculates the sum of all values in the window, then divides by the size of the window. It is effectively
a simple arithmetic mean of the window. The simple moving average does not perform any time-dependent weighting, which means
the values from a simple
moving average tend to "lag" behind the real data.
null
and NaN
values are ignored; the average is only calculated over the real values. If the window is empty, or all values are
null
/NaN
, NaN
is returned as the result. This means that the count used in the average calculation is count of non-null
,non-NaN
values.
Table 20. unweightedAvg(double[] values)
Parameters
Parameter Name | Description |
---|---|
|
The window of values to find the sum of |
POST /_search { "size": 0, "aggs": { "my_date_histo":{ "date_histogram":{ "field":"date", "calendar_interval":"1M" }, "aggs":{ "the_sum":{ "sum":{ "field": "price" } }, "the_movavg": { "moving_fn": { "buckets_path": "the_sum", "window": 10, "script": "MovingFunctions.unweightedAvg(values)" } } } } } }
linearWeightedAvg Function
editThe linearWeightedAvg
function assigns a linear weighting to points in the series, such that "older" datapoints (e.g. those at
the beginning of the window) contribute a linearly less amount to the total average. The linear weighting helps reduce
the "lag" behind the data’s mean, since older points have less influence.
If the window is empty, or all values are null
/NaN
, NaN
is returned as the result.
Table 21. linearWeightedAvg(double[] values)
Parameters
Parameter Name | Description |
---|---|
|
The window of values to find the sum of |
POST /_search { "size": 0, "aggs": { "my_date_histo":{ "date_histogram":{ "field":"date", "calendar_interval":"1M" }, "aggs":{ "the_sum":{ "sum":{ "field": "price" } }, "the_movavg": { "moving_fn": { "buckets_path": "the_sum", "window": 10, "script": "MovingFunctions.linearWeightedAvg(values)" } } } } } }
ewma Function
editThe ewma
function (aka "single-exponential") is similar to the linearMovAvg
function,
except older data-points become exponentially less important,
rather than linearly less important. The speed at which the importance decays can be controlled with an alpha
setting. Small values make the weight decay slowly, which provides greater smoothing and takes into account a larger
portion of the window. Larger values make the weight decay quickly, which reduces the impact of older values on the
moving average. This tends to make the moving average track the data more closely but with less smoothing.
null
and NaN
values are ignored; the average is only calculated over the real values. If the window is empty, or all values are
null
/NaN
, NaN
is returned as the result. This means that the count used in the average calculation is count of non-null
,non-NaN
values.
Table 22. ewma(double[] values, double alpha)
Parameters
Parameter Name | Description |
---|---|
|
The window of values to find the sum of |
|
Exponential decay |
POST /_search { "size": 0, "aggs": { "my_date_histo":{ "date_histogram":{ "field":"date", "calendar_interval":"1M" }, "aggs":{ "the_sum":{ "sum":{ "field": "price" } }, "the_movavg": { "moving_fn": { "buckets_path": "the_sum", "window": 10, "script": "MovingFunctions.ewma(values, 0.3)" } } } } } }
holt Function
editThe holt
function (aka "double exponential") incorporates a second exponential term which
tracks the data’s trend. Single exponential does not perform well when the data has an underlying linear trend. The
double exponential model calculates two values internally: a "level" and a "trend".
The level calculation is similar to ewma
, and is an exponentially weighted view of the data. The difference is
that the previously smoothed value is used instead of the raw value, which allows it to stay close to the original series.
The trend calculation looks at the difference between the current and last value (e.g. the slope, or trend, of the
smoothed data). The trend value is also exponentially weighted.
Values are produced by multiplying the level and trend components.
null
and NaN
values are ignored; the average is only calculated over the real values. If the window is empty, or all values are
null
/NaN
, NaN
is returned as the result. This means that the count used in the average calculation is count of non-null
,non-NaN
values.
Table 23. holt(double[] values, double alpha)
Parameters
Parameter Name | Description |
---|---|
|
The window of values to find the sum of |
|
Level decay value |
|
Trend decay value |
POST /_search { "size": 0, "aggs": { "my_date_histo":{ "date_histogram":{ "field":"date", "calendar_interval":"1M" }, "aggs":{ "the_sum":{ "sum":{ "field": "price" } }, "the_movavg": { "moving_fn": { "buckets_path": "the_sum", "window": 10, "script": "MovingFunctions.holt(values, 0.3, 0.1)" } } } } } }
In practice, the alpha
value behaves very similarly in holtMovAvg
as ewmaMovAvg
: small values produce more smoothing
and more lag, while larger values produce closer tracking and less lag. The value of beta
is often difficult
to see. Small values emphasize long-term trends (such as a constant linear trend in the whole series), while larger
values emphasize short-term trends.
holtWinters Function
editThe holtWinters
function (aka "triple exponential") incorporates a third exponential term which
tracks the seasonal aspect of your data. This aggregation therefore smooths based on three components: "level", "trend"
and "seasonality".
The level and trend calculation is identical to holt
The seasonal calculation looks at the difference between
the current point, and the point one period earlier.
Holt-Winters requires a little more handholding than the other moving averages. You need to specify the "periodicity"
of your data: e.g. if your data has cyclic trends every 7 days, you would set period = 7
. Similarly if there was
a monthly trend, you would set it to 30
. There is currently no periodicity detection, although that is planned
for future enhancements.
null
and NaN
values are ignored; the average is only calculated over the real values. If the window is empty, or all values are
null
/NaN
, NaN
is returned as the result. This means that the count used in the average calculation is count of non-null
,non-NaN
values.
Table 24. holtWinters(double[] values, double alpha)
Parameters
Parameter Name | Description |
---|---|
|
The window of values to find the sum of |
|
Level decay value |
|
Trend decay value |
|
Seasonality decay value |
|
The periodicity of the data |
|
True if you wish to use multiplicative holt-winters, false to use additive |
POST /_search { "size": 0, "aggs": { "my_date_histo":{ "date_histogram":{ "field":"date", "calendar_interval":"1M" }, "aggs":{ "the_sum":{ "sum":{ "field": "price" } }, "the_movavg": { "moving_fn": { "buckets_path": "the_sum", "window": 10, "script": "if (values.length > 5*2) {MovingFunctions.holtWinters(values, 0.3, 0.1, 0.1, 5, false)}" } } } } } }
Multiplicative Holt-Winters works by dividing each data point by the seasonal value. This is problematic if any of
your data is zero, or if there are gaps in the data (since this results in a divid-by-zero). To combat this, the
mult
Holt-Winters pads all values by a very small amount (1*10-10) so that all values are non-zero. This affects
the result, but only minimally. If your data is non-zero, or you prefer to see NaN
when zero’s are encountered,
you can disable this behavior with pad: false
"Cold Start"
editUnfortunately, due to the nature of Holt-Winters, it requires two periods of data to "bootstrap" the algorithm. This
means that your window
must always be at least twice the size of your period. An exception will be thrown if it
isn’t. It also means that Holt-Winters will not emit a value for the first 2 * period
buckets; the current algorithm
does not backcast.
You’ll notice in the above example we have an if ()
statement checking the size of values. This is checking to make sure
we have two periods worth of data (5 * 2
, where 5 is the period specified in the holtWintersMovAvg
function) before calling
the holt-winters function.
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