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rolling-max

Input Stack:
w: Window
expr: TimeSeriesExpr
 ⇨ Output Stack:
TimeSeriesExpr
 

Find the maximum value within a rolling window of datapoints. This operation is useful for tracking peak values, establishing upper bounds for noisy data, and creating envelope functions that follow the highest points in fluctuating signals.

Parameters

  • expr: The time series expression to find rolling maximum 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

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 and the input is passed through unchanged. The window must be positive.

Behavior

  • Maximum selection: Returns the highest value in the current window
  • Missing data: NaN values are ignored in the calculation
  • All NaN window: If all values in window are NaN, emits NaN

Alerting Example

Using rolling max to establish recent peak baseline:

name,sps,:eq,:sum,
:dup,
5,:rolling-max

This creates both the current value and its recent maximum for comparison.

Data Processing Example

Input 3,:rolling-max
0 0
1 1
-1 1
NaN 1
0 0
1 1
1 1
1 1
1 1
0 1

Examples

5-point rolling maximum:

BeforeAfter
:random,
0.4,:gt

:random,
0.4,:gt,
5,:rolling-max

Rolling maximum over the last 15 minutes, independent of the step size:

BeforeAfter
name,sps,:eq,
:sum

name,sps,:eq,
:sum,
15m,:rolling-max

Since: 1.6