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From: "Tim " <tim.bate@gmail.com>
Newsgroups: comp.soft-sys.matlab
Subject: Re: weighted average time series
Date: Wed, 10 Mar 2010 02:53:05 +0000 (UTC)
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I think the amplitude is one obvious difference, but I'm assuming you mean the shape as well?

Honestly, normalizing seems the best way, the question is how. Normalize so the max = 1, or the sum = 1, is probably right. But without knowledge of how the signal works (is it "adding", hence a gain problem, like ImageAnalyst says? or is it multiplying, so we need to consider a log scale?) The picture doesn't tell enough (actually, it does if I measure, but not gonna) to know.

You say you tried some things, but no cigar? Did you try normalizing? To simplify it, if you like, you could use "buckets," since what you have is basically a histogram. If not a histogram exactly, then you could certainly treat it like one.

Of course, once they are all normalized, you would probably be taking differences, just like ImageAnalyst says. I can't really see why normalizing and then difference wouldn't work, simple as it may seem.

"dormant" <rod.stewart@uwiseismic.com> wrote in message <hn0skl$ppc$1@fred.mathworks.com>...
> I am sure that what I want to do is simple in MATLAB, but my maths has totally failed me and Help is driving me round in circles. Can anyone help?
> 
> I have ten time series', all sampled over the same time interval, all trying to measure the same thing. All ten share a similar shape, but all of them show departures from it. All ten have a very different amplitude range, but they all start from 0. 
> 
> In case my description isn't enough, the time series' look like this (ignore the red one):
> http://www.pbase.com/dormant/image/122549911
> 
> I want to combine them together to construct a weighted-average time series.  So I need to determine one average time series and ten weights.
> 
> I thought this would be a least-squares problem, but I can't seem to get it in the right form.