holtWinters() function

*Function type: Transformation
**Output data type:* Float

The Holt-Winters method predicts seasonally-adjusted values for the specified column at the specified . 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 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.

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 is set to true.

Null timestamps

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*

seasonality

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

*Data type: Integer*

The interval between two data points.

withFit

Return fitted data in results. Defaults to false.

*Data type: Boolean*

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

*Data type: String*

column

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

*Data type: String*

Examples

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

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