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On 14/04/11 11:57, Mike Marchywka wrote:

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>> Date: Thu, 14 Apr 2011 11:29:23 +0200

>> From:

[hidden email]
>> To:

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>> Subject: [R] Identify period length of time series automatically?

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>> Hi

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>> I have 10.000 simulations for a sensitivity analysis. I have done a few

>> sensitivity analysis for different response variables already,

>> but now, as most of the simulations (if not all) show some cyclic

>> behaviour, see how the independent input parameter influence the

>> frequency of the cyclic changes and "how cyclic" they actually are.

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>> So effectively, I have 39 values, and I want to identify automatically

>> the frequency / period length of the series and a kind of a measure on

>> "how cyclic" the series is.

Hi Mike,

thanks for your answer - it confirms my fears ...

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> Probably google "Digital Signal Processing" or Fourier transform.

> From this, you resolve your time series into sinusoids of various components

> and you can separate peaks in line spectra from background noise.

> Depending on what you consider to be "cyclic" the analysis details

> will vary. If you look at things like amplitude and frequncy modulation

> of one sine wave with another and various relationships between carrier and

> modulation frequency, you can get some ideas of what to look for in spectra.

That is what I thought as well. As I have no idea about fourier

analysis, could you give me a small example in R, which gives me the

frequencies of the resulting sin waves after a fourier transformation?

I only see large matrices as return values when using e.g. fft().

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> Alternatively, you can try to define exactly what you mean by "cyclic"

> and maybe make a better transform that discriminates that from acyclic

> but offhand I would suggest FFT and various tests on the spectra.

the shape of the fluctuations can be quite different - so no common

pattern there.

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> Just off hand I'm not sure that 39 points would be a lot to go on

> but you can simulate some examples in R quite easily if you know

> what the data looks like in various cases you think may exist.

Well - the data is over a year summed up data from daily data points, so

I could easily go to daily data, which would be 365*39. But that would

make the analysis probably more difficult, as I have seasonal

fluctuations, and fluctuations over several years (1, 2, 3, 4, ...?;

depending on the parameters used for the simulation).

Any ideas on how to do this in R?

I have the feeling, that the quesion id more difficult then I thought...

Rainer

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>> How can I do that automatically without individual checking? I do not

>> want to do an eyeball assessment for 10.000 time series....

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>> Thanks,

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>> Rainer

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>> - --

>> Rainer M. Krug, PhD (Conservation Ecology, SUN), MSc (Conservation

>> Biology, UCT), Dipl. Phys. (Germany)

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>> Centre of Excellence for Invasion Biology

>> Stellenbosch University

>> South Africa

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- --

Rainer M. Krug, PhD (Conservation Ecology, SUN), MSc (Conservation

Biology, UCT), Dipl. Phys. (Germany)

Centre of Excellence for Invasion Biology

Stellenbosch University

South Africa

Tel : +33 - (0)9 53 10 27 44

Cell: +33 - (0)6 85 62 59 98

Fax : +33 - (0)9 58 10 27 44

Fax (D): +49 - (0)3 21 21 25 22 44

email:

[hidden email]
Skype: RMkrug

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