# Reverse-engineer fractal resampling process

by deadrabbit
Tags: fractal, process, resampling, reverseengineer
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 P: 1 Hey, I am working on a project where I need to take several time series of various lengths and identify common features. So, for example, a period of 100 days may exhibit the same features as a period of 10 days -- the system is self-similar in this way. In order to compare these series of different lengths I need to strip out noise that is not important for feature identification in order to bring them to the same scale. I have come across this document that shows a rather efficient method of doing this and would like to reverse engineer it... any help greatly appreciated. http://www.congrexprojects.com/docs/...resampling.pdf
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 Quote by deadrabbit Hey, I am working on a project where I need to take several time series of various lengths and identify common features. So, for example, a period of 100 days may exhibit the same features as a period of 10 days -- the system is self-similar in this way. In order to compare these series of different lengths I need to strip out noise that is not important for feature identification in order to bring them to the same scale. I have come across this document that shows a rather efficient method of doing this and would like to reverse engineer it... any help greatly appreciated. http://www.congrexprojects.com/docs/...resampling.pdf
Why don't you just contact the authors of the work to ask for their help?
 Engineering Sci Advisor HW Helper Thanks P: 7,172 Doesn't page 8 already explain it? If the original data set is ##x_0, x_1, \dots##, start by keeping the points ##x_0, x_{2^k}, 2x_{2^k}, \dots## for a "large" value of ##k##. If linear interpolation between those points is not good enough in an interval, add the mid-point of that interval to the list of points. Rinse and repeat till the result is accurate enough. In the example they start from ##x_0## and ##x_8##, then add the mid point ##x_4##, etc. You might want to compare this will something like spline fitting adaptive knot placement, e.g. http://www3.stat.sinica.edu.tw/stati...pdf/A20n39.pdf For the "inspiration" on page 7, google fractal (or fractional) brownian terrain generation.

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