holtWinters() function

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

Function type: Transformation
Output data type: Float

  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 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:

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.



The number of values to predict.

Data type: Integer


The number of points in a season. Defaults to 0.

Data type: Integer


The interval between two data points.

Data type: Duration


Return fitted data in results. Defaults to false.

Data type: Boolean


The time column to use. Defaults to "_time".

Data type: String


The column to operate on. Defaults to "_value".

Data type: String


Use aggregateWindow to prepare data for holtWinters
from(bucket: "example-bucket")
    |> range(start: -7y)
    |> filter(fn: (r) => r._field == "water_level")
    |> aggregateWindow(every: 379m, fn: first).
    |> holtWinters(n: 10, seasonality: 4, interval: 379m)

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