added holtWinters function

content/v2.0/reference/flux/functions/built-in/transformations/aggregates/doubleema.md

@@ -27,7 +27,7 @@ _**Function type:** Aggregate_
 doubleEMA(n: 5)
 ```
 
-##### Double exponential moving average rules:
+##### Double exponential moving average rules
 - A double exponential moving average is defined as `doubleEMA = 2 * EMA_N - EMA of EMA_N`.
     - `EMA` is an exponential moving average.
     - `N = n` is the period used to calculate the EMA.

content/v2.0/reference/flux/functions/built-in/transformations/aggregates/exponentialmovingaverage.md

@@ -25,7 +25,7 @@ _**Function type:** Aggregate_
 exponentialMovingAverage(n: 5)
 ```
 
-##### Exponential moving average rules:
+##### Exponential moving average rules
 - The first value of an exponential moving average over `n` values is the
   algebraic mean of `n` values.
 - Subsequent values are calculated as `y(t) = x(t) * k + y(t-1) * (1 - k)`, where:

content/v2.0/reference/flux/functions/built-in/transformations/aggregates/holtwinters.md

@@ -0,0 +1,114 @@
+---
+title: holtWinters() function
+description: >
+  The `holtWinters()` function applies the Holt-Winters forecasting method to input tables.
+aliases:
+  - /v2.0/reference/flux/functions/transformations/aggregates/holtwinters
+menu:
+  v2_0_ref:
+    name: holtWinters
+    parent: built-in-aggregates
+weight: 501
+related:
+  - https://docs.influxdata.com/influxdb/latest/query_language/functions/#holt-winters, InfluxQL HOLT_WINTERS()
+---
+
+The `holtWinters()` function applies the Holt-Winters forecasting method to input tables.
+
+_**Function type:** Aggregate_  
+_**Output data type:** Float_
+
+```js
+holtWinters(
+  n: 10,
+  seasonality: 4,
+  interval: 30d,
+  withFit: false,
+  timeColumn: "_time",
+  column: "_value",
+)
+```
+
+The Holt-Winters method predicts [`n`](#n) seasonally-adjusted values for the
+specified [`column`](#column) at the specified [`interval`](#interval).
+For example, if `interval` is `6m` and `n` is `3`, results include three predicted
+values six minutes apart.
+
+#### Seasonality
+[`seasonality`](#seasonality) delimits the length of a seasonal pattern according to `interval`.
+If your `interval` is `2m` and `s` is `3`, then the seasonal pattern occurs every
+six minutes or every three data points.
+If there is no seasonality in the data, set `seasonality` to `0`.
+
+#### Space values evenly in time
+`holtWinters()` expects values evenly spaced in time.
+To ensure this, it applies the following rules:
+
+- `interval` is used to divide the into buckets based time.
+- 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.
+Use [`window()`](/v2.0/reference/flux/functions/built-in/transformations/window/)
+and [selectors](/v2.0/reference/flux/functions/built-in/transformations/selectors/)
+or [aggregates](/v2.0/reference/flux/functions/built-in/transformations/aggregates/),
+or use [`aggregateWindow()`](/v2.0/reference/flux/functions/built-in/transformations/aggregates/aggregatewindow)
+to specify other values to use in the `holtWinters()` calculation.
+
+#### Fitted model
+The `holtWinters()` function applies the [Nelder-Mead optimization](https://en.wikipedia.org/wiki/Nelder%E2%80%93Mead_method)
+to include "fitted" data points in results when [`withFit`](#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
+The number of values to predict.
+
+_**Data type: Integer**_
+
+### seasonality
+The number of points in a season.
+Defaults to `0`.
+
+_**Data type: Integer**_
+
+### interval
+The interval between two data points.
+
+_**Data type: Duration**_
+
+### withFit
+Return [fitted data](#fitted-model) in results.
+Defaults to `false`.
+
+_**Data type: Boolean**_
+
+### timeColumn
+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
+```js
+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)
+```

content/v2.0/reference/flux/functions/built-in/transformations/aggregates/movingaverage.md

@@ -24,7 +24,7 @@ _**Function type:** Aggregate_
 movingAverage(n: 5)
 ```
 
-##### Moving average rules:
+##### Moving average rules
 - The average over a period populated by `n` values is equal to their algebraic mean.
 - The average over a period populated by only `null` values is `null`.
 - Moving averages skip `null` values.

content/v2.0/reference/flux/functions/built-in/transformations/aggregates/relativestrengthindex.md

@@ -27,7 +27,7 @@ relativeStrengthIndex(
 )
 ```
 
-##### Relative strength index rules:
+##### Relative strength index rules
 - The general equation for calculating a relative strength index (RSI) is
   `RSI = 100 - (100 / (1 + (AVG GAIN / AVG LOSS)))`.
 - For the first value of the RSI, `AVG GAIN` and `AVG LOSS` are averages of the `n` period.

content/v2.0/reference/flux/functions/built-in/transformations/aggregates/tripleema.md

@@ -29,7 +29,7 @@ _**Function type:** Aggregate_
 tripleEMA(n: 5)
 ```
 
-##### Triple exponential moving average rules:
+##### Triple exponential moving average rules
 - A triple exponential moving average is defined as `tripleEMA = (3 * EMA_1) - (3 * EMA_2) + EMA_3`.
   - `EMA_1` is the exponential moving average of the original data.
   - `EMA_2` is the exponential moving average of `EMA_1`.