double_exponential_smoothing()
Use the double_exponential_smoothing() experimental function to smooth a time series
based on the importance you assign to older data and possible trends. See the
PromQL documentation.
ts_of_*(range-vector)
Use the ts_of_... (timestamp of) experimental functions to aggregate timestamps
of samples within a specified interval. For details, see
<aggregation>_over_time()
in the Prometheus documentation.
These experimental functions were introduced by specific Prometheus versions:
| Function name | Description | Version added |
|---|---|---|
| ts_of_first_over_time | Returns the timestamp of the earliest sample in the range vector for each series. | 3.7.0 |
| ts_of_last_over_time | Returns the timestamp of the last sample in the specified interval, per series. | 3.5.0 |
| ts_of_min_over_time | Returns the timestamp of the last float sample that has the minimum value of all float samples in the specified interval, per series. | 3.5.0 |
| ts_of_max_over_time | Returns the timestamp of the last float sample that has the maximum value of all float samples in the specified interval, per series. | 3.5.0 |
holt_winters() (deprecated)
As of Prometheus 3.0, this function is named double_exponential_smoothing().
Although Observability Platform aliases holt_winters() to provide backward
compatibility, you should replace it with the new name to avoid issues when
Observability Platform removes the holt_winters() alias.