Rolling Statistics

Use the Database Tools > Filters > Rolling Statistics menu option (ROLLINGSTATS GX) to calculate statistics on the values in a window surrounding individual data points.

The option is also available in the Moving Platform Gravity > 1D Filters menu.

Rolling Statistics dialog options

Channel to analyze	From the list of channels in the current database, select the channel to analyze. Script Parameter: ROLLINGSTATS.IN
Output statistics channel	Specify the output channel that will contain the results (it will be created if it does not exist). Script Parameter: ROLLINGSTATS.OUT
Statistic to calculate	Select one of the available methods from the drop-down list. See the table below (in the Application Notes section) for the list of available statistics. Script Parameter: ROLLINGSTATS.STATISTIC – See the table below.
Window width	Specify the width of the rolling window. If this value is even, it is increased by 1 to obtain a window that is symmetric about each data point. Script Parameter: ROLLINGSTATS.WIDTH
Shrink window at ends	Choose whether to shrink window at ends. If "Yes" is selected, the window shrinks near the ends so that it remains of equal size on both sides of the individual data points. This can eliminate edge effects. Script Parameter: ROLLINGSTATS.SHRINK 0:No, 1:Yes

Application Notes

If the window size is even, it is increased by 1 so that the output value is put at the exact centre index of the window.

Statistics are calculated on the values in a window surrounding the individual data points.

By shrinking the window at the ends, one-sided effects can be eliminated. For instance, if the data is linear to begin with, a rolling mean will not alter the original data. However, if the window size is kept constant, values near the ends tend to be pulled up or down.

With shrinking, the window is shrunk so that it always has the same width on both sides of the data point under analysis; at the end points the window width is 1, at the next point in it is 3, and so on, until the full width is reached.

The median value is calculated by sorting the valid data in the window, then selecting the middle value. If the number of valid data points is even, then the average of the two central values is returned.

The mode value is defined as the value that occurs most frequently in the data. This value may not even exist, or may not be unique.

In this implementation, the following algorithm is used:

The following statistics are supported:

Statistic	Explanation	Script value
Number of items	Total items – dummy items	0
Items > 0	Positive items	1
Dummy values		2
Minimum	Minimum value	3
Maximum	Maximum value	4
Range	Maximum - minimum	5
Mean	Sum / Valid items	6
Median	Middle of sorted values (See note above)	7
Mode	Most frequently occurring value (See note above)	8
Geometric Mean	Uses only positive values.	9
Variance		10
Standard Deviation		11
Standard Error		12
Skew	Normalized 3rd moment	13
Kurtosis	Normalized 4th moment	14
First value	Base (1st) value in the window	15
Sum		16
Sum of squares		17
Sum of cubes		18
Sum of 4th powers		19