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title description menu weight flux/v0/tags introduced
holtWinters() function `holtWinters()` applies the Holt-Winters forecasting method to input tables.
flux_v0_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 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

*tag _time _value
t1 2021-01-01T00:01:00Z 10.955056783457671
t1 2021-01-01T00:01:10Z 10.929416756584807
t1 2021-01-01T00:01:20Z 10.913930733843678
t1 2021-01-01T00:01:30Z 10.904983736080599
t1 2021-01-01T00:01:40Z 10.89992727039632
t1 2021-01-01T00:01:50Z 10.897102216255147
*tag _time _value
t2 2021-01-01T00:01:00Z 6.791274364986771
t2 2021-01-01T00:01:10Z 6.791274496071822
t2 2021-01-01T00:01:20Z 6.791274497934493
t2 2021-01-01T00:01:30Z 6.791274497960961
t2 2021-01-01T00:01:40Z 6.791274497961337
t2 2021-01-01T00:01:50Z 6.791274497961342

{{% /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 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

*tag _time _value
t1 2021-01-01T00:01:00Z 4.689501667763805
t1 2021-01-01T00:01:10Z 9.89174309681615
t1 2021-01-01T00:01:20Z 3.586846840716385
t1 2021-01-01T00:01:30Z 6.51792718960969
*tag _time _value
t2 2021-01-01T00:01:00Z 0.0003493824987647374
t2 2021-01-01T00:01:10Z 12.80532295591006
t2 2021-01-01T00:01:20Z 8.6277743213372
t2 2021-01-01T00:01:30Z 1.4602203332390207

{{% /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 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

*tag _time _value
t1 2021-01-01T00:00:00Z -2
t1 2021-01-01T00:00:10Z 9.22029445567278
t1 2021-01-01T00:00:20Z 10.72226472734998
t1 2021-01-01T00:00:30Z 11.027320524107417
t1 2021-01-01T00:00:40Z 11.036623518669842
t1 2021-01-01T00:00:50Z 10.993492350404422
t1 2021-01-01T00:01:00Z 10.955056783457671
t1 2021-01-01T00:01:10Z 10.929416756584807
t1 2021-01-01T00:01:20Z 10.913930733843678
*tag _time _value
t2 2021-01-01T00:00:00Z 19
t2 2021-01-01T00:00:10Z 10.099938333168268
t2 2021-01-01T00:00:20Z 3.576640457126943
t2 2021-01-01T00:00:30Z 6.744937158136209
t2 2021-01-01T00:00:40Z 6.79061592841652
t2 2021-01-01T00:00:50Z 6.791265139903281
t2 2021-01-01T00:01:00Z 6.791274364986771
t2 2021-01-01T00:01:10Z 6.791274496071822
t2 2021-01-01T00:01:20Z 6.791274497934493

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