rolling-mean
Input Stack:| minNumValues: Int | | w: Window | | expr: TimeSeriesExpr |
|
⇨ |
Output Stack: |
Compute a rolling average over a specified window of datapoints. The mean is only calculated
when there are sufficient non-NaN values in the window, making it robust against missing data.
This is useful for smoothing noisy data while maintaining data quality requirements.
Parameters
- expr: The time series expression to compute the rolling mean for
- w: Window size, either a number of datapoints including the current value, such as
5, or a duration, such as 5m. See Window Size
- minNumValues: Minimum required non-NaN values to compute the mean
Window Size
The window can be specified as a number of datapoints or as an amount of time. A number of
datapoints, 5, will consider the last 5 values, including the current value. The amount of
time covered then changes with the step size, for example when zooming out to a time frame that
causes consolidation. A duration, 5m, will consider the values for the last 5 minutes
independent of the step size. The duration is rounded down to the nearest step boundary, so a
5m window with a 2m step will use a 4m window with two datapoints. If the step size is larger
than the window, then the window will be a single datapoint. The window must be positive.
Window Behavior
- Quality threshold: Requires minimum non-NaN values before emitting a result
- Missing data handling: NaN values are ignored in calculation but count toward window size
- Consolidation impact: With a number of datapoints, each datapoint represents a longer time
window when the step size increases. Use a duration to keep the time covered fixed
- Small windows: If
minNumValues is larger than the number of datapoints in the window,
for example a duration window at a large step size, the result is NaN
Data Processing Example
| Input |
3,2,:rolling-mean |
| 0 |
NaN |
| 1 |
0.5 |
| -1 |
0.0 |
| NaN |
0.0 |
| NaN |
NaN |
| 0 |
NaN |
| 1 |
0.5 |
| 1 |
0.667 |
| 1 |
1 |
| 0 |
0.667 |
Examples
5-point rolling mean with minimum 3 values:
Since: 1.6
