[scilab-Users] saisonality in time series

[Charles Warner]

Another trick I have found that greatly reduces FFT noise it to temporarily
mask any localized "spikes" in the data (such spikes, with a narrow
temporal profile have a very broad spectral distribution).  One can also
try to eliminate any offset by subtracting the mean (or the geometric mean
or harmonic mean- the appropriate mean would be dictated by the nature of
the data).  This should hopefully reduce the scale of the FFT amplitude,
making it easier to spot any (especially low-frequency, or seasonal)
potential frequency components.

On Fri, Nov 18, 2011 at 3:09 AM, Schreckenbach Stephan <
s.schreckenbach at truma.com> wrote:

> **
>
> Hi,****
>
> ** **
>
> sorry, of course I meant seasonality.****
>
> The time series consists of longer term trends, short term noise and short
> time seasonality. ****
>
> oscillations / seasonality, if any, it is most likely to be nonharmonic. I
> look for distinct frequencies.****
>
> When I did a FFT plot of the original time series there was noise only in
> the spectrum.****
>
> I will give it a run with the differenciated series / the log of the data.
> ****
>
> There is still the question how to test for significance of the found
> seasonality. ****
>
> ** **
>
> Stephan****
>
>  ****
>
>  ****
>   ------------------------------
>
> *Von:* Charles Warner [mailto:cwarner.cw711 at gmail.com]
> *Gesendet:* Freitag, 18. November 2011 00:34
> *An:* users at lists.scilab.org
> *Betreff:* Re: [scilab-Users] saisonality in time series****
>
> ** **
>
> Although "seasonality" is not the term I use for long term trends hidden
> in noisy data, I have had some success by taking the log of the data, and
> running an FFT on the log data.  Usually, I have some prior knowledge of
> the long-term periodic trends I expect, so it is relatively easy to
> determine quickly if this method works.  Plotting the log of the data also
> gives one a good feel for whether the data is stationary, or whether there
> are windows of data that can be treated as stationary.  Any changing
> magnitude effect is, of course, reduced when on works with logs, but such
> effects can help one understand what the raw data is really telling you.
>
> Charlie****
>
> On Thu, Nov 17, 2011 at 12:40 PM, Mike Page <Mike at page-one.waitrose.com>
> wrote:****
>
> Hi,
>
> I don't know much about this application, but the Cepstrum can be used to
> find hidden periodicity in time series.  Might be worth trying?  I have
> used
> it for finding rotational components in the vibration signatures from
> rotating machinery.  There's a simple example here
> (http://www.dliengineering.com/downloads/cepstrum%20analysis.pdf).
>
> Mike.****
>
>
>
> -----Original Message-----
> From: Petter Wingren [mailto:petterwr at gmail.com]
> Sent: 17 November 2011 17:18
> To: users at lists.scilab.org
> Subject: Re: [scilab-Users] saisonality in time series
>
>
> Did a quick search but couldnt find anything obvious. I suppose the
> word you are looking for is seasonality - maybe that helps in finding
> something useful.
>
> On Thu, Nov 17, 2011 at 3:36 PM, **Schreckenbach Stephan**
> <s.schreckenbach at truma.com> wrote:
> >
> > Hi,
> >
> > I look for a test of saisonality in time series.
> > The time series might be instationary and nonlinear and the saisonality
> > / oscillation might have a changing amplitude. Furthermore the
> > distribution
> > might be unknown as well.
> > I need something to test for significant saisonality without knowing /
> > estimating a (linear) model of the time series.
> >
> > ideas I got so far: Chi Square Test for independency:
> > I could test for independence of saison and mean value of the data
> >
> > Chi Square Test to test for different means of two data groups.
> > I could test for a difference of the mean between several seasons.
> >
> > Any more or better ideas?
> >
> > Thanks in advance, Stephan
> >
> >
>
> ****
>
> ** **
>
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