Influxdata
/
docs-v2
mirror of https://github.com/influxdata/docs-v2.git
1
0
Fork 0
Code Issues Projects Releases Wiki Activity
docs-v2/content/flux/v0.x/stdlib/universe/holtwinters.md

9.8 KiB
Raw Permalink Blame History

title description menu weight flux/v0.x/tags introduced
holtWinters() function `holtWinters()` applies the Holt-Winters forecasting method to input tables.
flux_0_x_ref
name parent identifier
holtWinters universe universe/holtWinters
101
transformations
0.38.0

holtWinters() applies the Holt-Winters forecasting method to input tables.

The Holt-Winters method predicts n seasonally-adjusted values for the specified column at the specified interval. For example, if interval is six minutes (6m) and n is 3, results include three predicted values six minutes apart.

Seasonality

seasonality delimits the length of a seasonal pattern according to interval. If the interval is two minutes (2m) and seasonality is 4, then the seasonal pattern occurs every eight minutes or every four data points. If your interval is two months (2mo) and seasonality is 4, then the seasonal pattern occurs every eight months or every four data points. If data doesn’t have a seasonal pattern, set seasonality to 0.

Space values at even time intervals

holtWinters() expects values to be spaced at even time intervales. To ensure values are spaced evenly in time, holtWinters() applies the following rules:

  • Data is grouped into time-based "buckets" determined by the interval.
  • If a bucket includes many values, the first value is used.
  • If a bucket includes no values, a missing value (null) is added for that bucket.

By default, holtWinters() uses the first value in each time bucket to run the Holt-Winters calculation. To specify other values to use in the calculation, use aggregateWindow to normalize irregular times and apply an aggregate or selector transformation.

Fitted model

holtWinters() applies the Nelder-Mead optimization to include "fitted" data points in results when withFit is set to true.

Null timestamps

holtWinters() discards rows with null timestamps before running the Holt-Winters calculation.

Null values

holtWinters() treats null values as missing data points and includes them in the Holt-Winters calculation.

Function type signature
(
    <-tables: stream[A],
    interval: duration,
    n: int,
    ?column: string,
    ?seasonality: int,
    ?timeColumn: string,
    ?withFit: bool,
    ?withMinSSE: bool,
) => stream[B] where A: Record, B: Record

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

Parameters

n

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

interval

({{< req >}}) Interval between two data points.

withFit

Return fitted data in results. Default is false.

column

Column to operate on. Default is _value.

timeColumn

Column containing time values to use in the calculating. Default is _time.

seasonality

Number of points in a season. Default is 0.

withMinSSE

Return minSSE data in results. Default is false.

minSSE is the minimum sum squared error found when optimizing the holt winters fit to the data. A smaller minSSE means a better fit. Examining the minSSE value can help understand when the algorithm is getting a good fit versus not.

tables

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

Examples

Use holtWinters to predict future values

import "sampledata"

sampledata.int()
    |> holtWinters(n: 6, interval: 10s)

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

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

*tag _time _value
t1 2021-01-01T00:01:00Z 10.955057903416249
t1 2021-01-01T00:01:10Z 10.929417869181442
t1 2021-01-01T00:01:20Z 10.913931836435774
t1 2021-01-01T00:01:30Z 10.9049848302089
t1 2021-01-01T00:01:40Z 10.899928358379444
t1 2021-01-01T00:01:50Z 10.89710330009353
*tag _time _value
t2 2021-01-01T00:01:00Z 6.798303631428536
t2 2021-01-01T00:01:10Z 6.798303631555017
t2 2021-01-01T00:01:20Z 6.798303631555328
t2 2021-01-01T00:01:30Z 6.798303631555329
t2 2021-01-01T00:01:40Z 6.798303631555329
t2 2021-01-01T00:01:50Z 6.798303631555329

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

Use holtWinters with seasonality to predict future values

import "sampledata"

sampledata.int()
    |> holtWinters(n: 4, interval: 10s, seasonality: 4)

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

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

*tag _time _value
t1 2021-01-01T00:01:00Z 4.6673129595221
t1 2021-01-01T00:01:10Z 9.937498954744212
t1 2021-01-01T00:01:20Z 3.59073602964091
t1 2021-01-01T00:01:30Z 6.523905777397829
*tag _time _value
t2 2021-01-01T00:01:00Z -0.000320397929233291
t2 2021-01-01T00:01:10Z 4.986867840774593
t2 2021-01-01T00:01:20Z 3.4989671640270354
t2 2021-01-01T00:01:30Z -0.05530077960375555

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

Use the holtWinters fitted model to predict future values

import "sampledata"

sampledata.int()
    |> holtWinters(n: 3, interval: 10s, withFit: true)

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

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

*tag _time _value
t1 2021-01-01T00:00:00Z -2
t1 2021-01-01T00:00:10Z 9.22029510454727
t1 2021-01-01T00:00:20Z 10.72226534418895
t1 2021-01-01T00:00:30Z 11.027321424181121
t1 2021-01-01T00:00:40Z 11.036624575789808
t1 2021-01-01T00:00:50Z 10.993493462224441
t1 2021-01-01T00:01:00Z 10.955057903416249
t1 2021-01-01T00:01:10Z 10.929417869181442
t1 2021-01-01T00:01:20Z 10.913931836435774
*tag _time _value
t2 2021-01-01T00:00:00Z 19
t2 2021-01-01T00:00:10Z 10.269974995821258
t2 2021-01-01T00:00:20Z 3.343711756293869
t2 2021-01-01T00:00:30Z 6.789784945101966
t2 2021-01-01T00:00:40Z 6.798282676851281
t2 2021-01-01T00:00:50Z 6.798303580010179
t2 2021-01-01T00:01:00Z 6.798303631428536
t2 2021-01-01T00:01:10Z 6.798303631555017
t2 2021-01-01T00:01:20Z 6.798303631555328

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