Polynomial fitting is problematic, so instead bootstraping of data will be tried. This week show the results, and the next two weeks show how it was done.
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Excellent! I’ve been looking for a good source on Random Walk functions for a while. Finding a good realistic algorithm that can exercise multiple frequency modes has so far alluded me. My specific application is instrument measurement, noise and drift. I look forward to your follow ups. Once I have time to digest, I may have further questions…
I am not sure what you mean by multiple frequency modes. Since that section of the video will not be out for two weeks, I can tell you what I did here.
I considered modeling the distribution, and the cross correlation of the deltaX and deltaY. This seemed very difficult and prone to error with the smallish dataset. Instead, I went for a simplistic bootstraping technique. There were N samples in the original, and I wanted to run out for M more steps. I simply drew M samples from the original N samples (with replacement). This is easy to do with the randi command to generate a random integer.
Would that work in your cases?