Updated 31 Dec 2020
Simple code for efficient calculation of online updates to moving/rolling or running variance and mean using Welford's method.
The purpose of this script is to compute very fast rolling variances and means in a stepwise way. This allows for online processing of incoming data. It uses a recursive algorithm to calculate online updates to the current std and mean. It is also capable of calculating rolling variances and means.
User script must hold onto a rolling buffer of values, which must be done outside of this class. See the demo script for an example. Note: Future improvements to this code should involve including this rolling buffer within the object. I haven't figured out how to do efficient setting of large array values within objects in Matlab, yet. If you know how to do it, please get in touch or send me a pull request! RichHakim@gmail.com
RUNNING (accumulating) vs. ROLLING (windowed): In order to use it as a running (accumulating from the first index) average or variance, just set win_size to be inf and vals_old to . This method can be used to calculate normal variances and means of arrays that are too big to fit into memory (like on a GPU)!
ARBITRARY DIMENSIONS: This script allows for an arbitrary number of dimensions to be used, but the rolling dimension must be dim 1.
Rich Hakim 2020. Most of this code uses a version of Welford's algorithm and was adapted from some Python code I found here: http://www.taylortree.com/2010/11/running-variance.html and here: http://www.taylortree.com/2010/06/running-simple-moving-average-sma.html and here: http://subluminal.wordpress.com/2008/07/31/running-standard-deviations/
Richard Hakim (2021). rolling-variance (https://github.com/RichieHakim/rolling-variance/releases/tag/v1.0), GitHub. Retrieved .
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