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Topic: Calculating a smooth 90% limit for differences in a time series
Replies: 1   Last Post: Mar 1, 2014 12:57 PM

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Richard Ulrich

Posts: 2,866
Registered: 12/13/04
Re: Calculating a smooth 90% limit for differences in a time series
Posted: Mar 1, 2014 12:57 PM
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On Fri, 28 Feb 2014 05:38:43 -0800 (PST), randomexpert@gmail.com
wrote:

>I have 50 data sets. Each set has three related time series: fast, medium, slow. My end purpose is simple, I want to generate a number that indicates a relative degree of change of the time series at each point. That relative degree of change should range between 0-1 for all the time series and all the data sets. The scales of the data set range from .0001 to 100.

I can't say that I understand your problem well.

However - given that the scales range fro 0.0001 to 100,
and that you want "relative degree of change", the very
first starting point would seem to be: Take the logarithm
of everything.

The standard deviation of scores, for log scaling, is
often used in various contexts as the same thing as
a coefficient of variation. It looks like you might have
something useful if you look at the SD of the change
scores.

- Are those consistent for different parts of a single
time series? If they change, you might not have
data consistent with the sort of model you are trying
for.


>
>To accomplish this, I calculate the differences in a time series, delta(ts)=ts(t) - ts(t-1). Now I am trying to calculate an upper limit of 90% of those deltas. In other words, draw a smooth line on those differences such that only about 10% of the differences exceed that line. I will use that 90% limit to establish a maximum to normalize the differences between 0-1. Is this the best way to do this?
>
>I've been working on this for months, mostly linear programmatic methods, with no success. And trying to get it to work across 50 data sets is killing me. I'm sure there has to be an elegant mathematical way to do this. I can't be the first guy in town trying to normalize a relative degree of change of a time series.
>
>Any help or directions for research are greatly appreciated! Obviously my math skills are weak so examples would be most helpful. Thank you everyone for your time and brain power!


Econometricians are the folks who I know of, who are
most concerned with time series and their changes.
You might look for that sort of reference, or that
sort of List for posting the question.

--
Rich Ulrich




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