Analysis of Time Varying Data
Analysis of Time Varying Data
(OP)
Suppose I have two measurements that vary in time, for instance, Temperature and Dew Point. I want to see if there is some kind of connection between them. I could just plot one vs. the other; result is a big amorphous blob. So I start computing some statistics. If I use CORREL in Excel (it's easy to do, that's why this isn't in the Excel forum!), my correlation is actually slightly negative, which tells me only a little (as one goes up or down, the other is likely to go the opposite direction as the first).
I vaguely remembered autocorrelation from my turbulence studies. That turns out to be a way to estimate if some variable repeats, and the period (or wavelength) of the repeating. Not really useful.
ANOVA and HSD seem to work well only if the measurements aren't sequentially related--that is, one measurement in say column A, row 823 of a spreadsheet is taken at precisely the same time as the second measurement in column B, row 823. Those two stats are out then I think.
Any other ideas?
I vaguely remembered autocorrelation from my turbulence studies. That turns out to be a way to estimate if some variable repeats, and the period (or wavelength) of the repeating. Not really useful.
ANOVA and HSD seem to work well only if the measurements aren't sequentially related--that is, one measurement in say column A, row 823 of a spreadsheet is taken at precisely the same time as the second measurement in column B, row 823. Those two stats are out then I think.
Any other ideas?





RE: Analysis of Time Varying Data
not sure why you think ANOVA doesn't work with sequential data (but then the last time i did an ANOVA analysis was in uni.). if this is the case, why not scatter the results (so that they are not cronologically ordered), but you have to keep the correlation between column A and B (ie, more row 823 to after 231).
RE: Analysis of Time Varying Data
RE: Analysis of Time Varying Data
In the case of dewpoint, you'd at least expect to see a upwardly sloping blob, since you'd expect there to be scatter in the RH relative to temperature, but for any given RH day, the dewpoint should loosely track with the ambient temperature.
TTFN
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RE: Analysis of Time Varying Data
As I said, the x-y plot is a blob.
I don't think I am looking for coherence in the data sets; I am looking for a correlation--data A does this, data B responds by doing this; that kind of thing--I don't think I am looking for frequency or wavelengths in data set A or data set B....but maybe I ave similar frequency distribution, does that suggest they arm--if two data sets hae related?
RE: Analysis of Time Varying Data
RE: Analysis of Time Varying Data
RE: Analysis of Time Varying Data
If you think the effect is lagging in time, try doing a series of x-y plots where you shift one data column forward or backward to create an x(t)-vs-y(t+delta) plot. The raw data will likely still show blobby behavior, so you may also need to filter by only plotting results from periods when x is decreasing (or increasing).
RE: Analysis of Time Varying Data
If you want to test the time based theory you could break the time histories up into frames of data and calculate the coherence between them, but you will need a large number of datapoints for that, and a signal analysis textbook to tell you how to work the coherence out.
Cheers
Greg Locock
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RE: Analysis of Time Varying Data
RE: Analysis of Time Varying Data
The only thing that's even remotely correlatible is for a given day, that day's RH will track with temperature over small periods of time.
see: http
TTFN
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RE: Analysis of Time Varying Data
Cheers
Greg Locock
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RE: Analysis of Time Varying Data
But, perhaps he was answering his own question by picking an example relationship that showed that his data had no correlation, rathe than one that does correlate.
TTFN
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RE: Analysis of Time Varying Data
Thus far, if I compute the correlation coefficient with Excel, my corrosion current seems to correlate as well with moisture as temperature and relative humidity correspond--not at all, that is. I'll keep looking, though I suspect the few choices of X that I have (temperature, moisture, etc.) won't do--perhaps instead of Y being a function of just temperature, Y is more likely a function of BOTH temperature and water content of the air.
RE: Analysis of Time Varying Data
RE: Analysis of Time Varying Data
RE: Analysis of Time Varying Data
I tried to do a two parameter curve fit on the data, using temp. and absolute humidity. what a disaster! I get a curve fit that has a low (0.2) R-squared (correlation coefficient, that is).
RE: Analysis of Time Varying Data
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TTFN
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