---
title: holtWinters() function
description: holtWinters() applies the Holt-Winters forecasting method to input tables.
url: https://docs.influxdata.com/flux/v0/stdlib/universe/holtwinters/
estimated_tokens: 4289
product: Flux
version: v0
---

# holtWinters() function

-   Flux 0.38.0+
-   View InfluxDB support

`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](https://en.wikipedia.org/wiki/Nelder%E2%80%93Mead_method) 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

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

For more information, see [Function type signatures](/flux/v0/function-type-signatures/).

## Parameters

### n

(Required) Number of values to predict.

### interval

(Required) 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](#use-holtwinters-to-predict-future-values)
-   [Use holtWinters with seasonality to predict future values](#use-holtwinters-with-seasonality-to-predict-future-values)
-   [Use the holtWinters fitted model to predict future values](#use-the-holtwinters-fitted-model-to-predict-future-values)

### Use holtWinters to predict future values

```js
import "sampledata"

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

[](#view-example-input-and-output)

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 |

### Use holtWinters with seasonality to predict future values

```js
import "sampledata"

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

[](#view-example-input-and-output)

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 |

### Use the holtWinters fitted model to predict future values

```js
import "sampledata"

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

[](#view-example-input-and-output)

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 |

#### Related

-   [InfluxQL HOLT\_WINTERS()](/influxdb/v1/query_language/functions/#holt-winters)

[transformations](/flux/v0/tags/transformations/)