Computes an aggregated value sliding over a single time series over time. E.g. with a configuration to sum 2 minute windows and a 5 values spaced at one minute intervals, values 1 and 2 are summed, then 2 and 3, then 3 and 4, etc. This can be used to smooth volatile time series.
Fields include:
| Name | Data Type | Required | Description | Default | Example |
|---|---|---|---|---|---|
| aggregator | String | Required | The aggregation function to use on each window. | null | sum |
| windowSize | String | Required | A TSDB style duration for the window. | null | 15m |
| infectiousNan | Boolean | Optional | Whether or not NaNs from the source data should infect each bucket when aggregating values. E.g. if one value out of 20 are NaN in a bucket and this is true, the bucket will return a NaN. If all values in a bucket are NaN then the result will be NaN regardless.. |
false | true |
Note that interpolation is not required here. If a "window" is missing data, it's simply skipped.
Example:
{
"aggregator": "avg",
"windowSize": "15m"
}