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rolling-sum

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

Compute the cumulative sum of values within a rolling window. This operation is useful for calculating totals over recent periods, tracking cumulative events, or smoothing data by summing neighboring values.

Parameters

  • expr: The time series expression to apply the rolling sum to
  • 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.

Note that the result is still based on datapoints, so the value will change with the step size even when using a duration to keep the amount of time covered fixed.

Window Behavior

  • Inclusive: Includes the current datapoint in the sum calculation
  • NaN handling: NaN values are treated as 0 for summation but count toward window size

Examples

Applying a 5-datapoint rolling sum:

BeforeAfter
name,sps,:eq,
:sum

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

Data Processing Example

Input 3,:rolling-sum
0 0.0
1 1.0
-1 0.0
NaN 0.0
NaN -1.0
NaN NaN
1 1.0
1 2.0
1 3.0
0 2.0

Since: 1.6