AW: [scilab-Users] saisonality in time series

[Schreckenbach Stephan]

Filtering temporal spikes is a good idea, since there are some of them.
I will try that.

The data sample as around 7000 data points, the frequency I look for is
around 1/10 * sample rate.

 

May be there are methods that are better suited for identifying
frequency components in that kind of data?

FFT always describes the time series by harmonic oszillations, which
might not work well

if oscillations are not (strictly) harmonic.

 

What about wavelets (don't know much about it yet, though)?

 

 

Stephan

 

 

________________________________

Von: Charles Warner [mailto:cwarner.cw711 at gmail.com] 
Gesendet: Samstag, 19. November 2011 05:12
An: users at lists.scilab.org
Betreff: Re: [scilab-Users] saisonality in time series

 

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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