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title description aliases menu weight flux/v0.x/tags related introduced
holtWinters() function The `holtWinters()` function applies the Holt-Winters forecasting method to input tables.
/influxdb/v2.0/reference/flux/functions/transformations/aggregates/holtwinters
/influxdb/v2.0/reference/flux/functions/built-in/transformations/aggregates/holtwinters/
/influxdb/v2.0/reference/flux/stdlib/built-in/transformations/aggregates/holtwinters/
/influxdb/v2.0/reference/flux/stdlib/built-in/transformations/holtwinters/
/influxdb/cloud/reference/flux/stdlib/built-in/transformations/holtwinters/
flux_0_x_ref
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holtWinters universe
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transformations
/{{< latest "influxdb" "v1" >}}/query_language/functions/#holt-winters, InfluxQL HOLT_WINTERS()
0.38.0

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

Output data type: Float

holtWinters(
    n: 10,
    seasonality: 4,
    interval: 30d,
    withFit: false,
    timeColumn: "_time",
    column: "_value",
)

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 your interval is two minutes (2m) and seasonality is 4, then the seasonal pattern occurs every eight minutes or every four data points. Likewise, 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 evenly in time

holtWinters() expects values evenly spaced in time. To ensure holtWinters() values are spaced evenly in time, the following rules apply:

  • 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:

Use aggregateWindow to normalize irregular times
data
  |> aggregateWindow(every: 1d, fn: first)
  |> holtWinters(n: 10, seasonality: 4, interval: 1d)

Fitted model

The holtWinters() function 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.

Parameters

n

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

seasonality

The number of points in a season. Default is 0.

interval

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

withFit

Return fitted data in results. Default is false.

timeColumn

The time column to use. Default is "_time".

column

The column to operate on. Default is "_value".

tables

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

Examples

{{% flux/sample-example-intro plural=true %}}

Use holtWinters to predict future values

import "sampledata"

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

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

Input data

{{% flux/sample "int" %}}

{{% /flex-content %}} {{% flex-content %}}

Output data
_time tag _value
2021-01-01T00:01:00Z t1 10.955834804389518
2021-01-01T00:01:10Z t1 10.930165921204969
2021-01-01T00:01:20Z t1 10.914688653595203
2021-01-01T00:01:30Z t1 10.905759343909201
2021-01-01T00:01:40Z t1 10.900719277060372
2021-01-01T00:01:50Z t1 10.897906726242955
_time tag _value
2021-01-01T00:01:00Z t2 6.781008791726221
2021-01-01T00:01:10Z t2 6.781069271640753
2021-01-01T00:01:20Z t2 6.781073869897851
2021-01-01T00:01:30Z t2 6.7810742195001135
2021-01-01T00:01:40Z t2 6.781074246080124
2021-01-01T00:01:50Z t2 6.781074248100982

{{% /flex-content %}} {{< /flex >}} {{% /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 input and output" %}} {{< flex >}} {{% flex-content %}}

Input data

{{% flux/sample "int" %}}

{{% /flex-content %}} {{% flex-content %}}

Output data
_time tag _value
2021-01-01T00:01:00Z t1 7.179098046049717
2021-01-01T00:01:10Z t1 17.01106624302682
2021-01-01T00:01:20Z t1 14.576432091790977
2021-01-01T00:01:30Z t1 6.968535480723005
_time tag _value
2021-01-01T00:01:00Z t2 0.008498516062705495
2021-01-01T00:01:10Z t2 4.311588701815885
2021-01-01T00:01:20Z t2 4.306517319025279
2021-01-01T00:01:30Z t2 2.473640466434541

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

Use the holtWinters fitted model to predict future values

import "sampledata"

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

{{% expand "View input and output" %}} {{< flex >}} {{% flex-content %}}

Input data

{{% flux/sample "int" %}}

{{% /flex-content %}} {{% flex-content %}}

Output data
_time tag _value
2021-01-01T00:00:00Z t1 -2.0
2021-01-01T00:00:10Z t1 9.218975712746163
2021-01-01T00:00:20Z t1 10.724838162080957
2021-01-01T00:00:30Z t1 11.02931521239947
2021-01-01T00:00:40Z t1 11.0379002238265
2021-01-01T00:00:50Z t1 10.994404043609528
2021-01-01T00:01:00Z t1 10.955834804389518
2021-01-01T00:01:10Z t1 10.930165921204969
2021-01-01T00:01:20Z t1 10.914688653595203
_time tag _value
2021-01-01T00:00:00Z t2 19.0
2021-01-01T00:00:10Z t2 8.907308429189435
2021-01-01T00:00:20Z t2 4.983321898435179
2021-01-01T00:00:30Z t2 6.633066160485693
2021-01-01T00:00:40Z t2 6.769755828568384
2021-01-01T00:00:50Z t2 6.780213338483446
2021-01-01T00:01:00Z t2 6.781008791726221
2021-01-01T00:01:10Z t2 6.781069271640753
2021-01-01T00:01:20Z t2 6.781073869897851

{{% /flex-content %}} {{< /flex >}} {{% /expand %}}