On Sat, 2003-09-27 at 15:00, Pradyumna S Upadrashta wrote:
> If one applies a linear filter, with a lowpass of 400Hz, is it possible
> for it to induce spurious autocorrelation (that persists essentially
> what seems like 'forever') in a time-series?

Was the filter a real-time filter (analog or digital), or was it a
frequency-domain filter applied after the fact?

The effect you describe is how most linear filters work, in practice;
the real-time ones are called "recursive".  Another name for them is
"infinite impulse response" filters (that should give you a hint).

...
> However, upon inspection of the power spectrum, I notice a roll-off
> behavior ~400Hz (orig sampling rate was 1017Hz) and the ACF and PACF
> display very odd behavior as mentioned. 

That's only 100 Hz you've got until you hit the Nyquist frequency (which
I'm sure you know).  Data near the Nyquist frequency, and near 0 Hz, are
both a bit biased if you're using an FFT anyway (are you using the fftw
libraries?  they're quite nice for this).

> Our data acquisition person
> insists that a standard low-pass filter was used, with a DC offset. If
> this is the case, then i'm unable to understand why a time-series that
> should be pure white noise, isn't... 

See above.

> The caveat of not being able to 'know for sure' when something is
> stationary a priori, is that one can't rely on the power spectrum since
> it assumes a stationary signal structure for its estimation! (or am I
> wrong about this?)

You are.  Read Bloomfield's introduction, with the notion of
"detrending" the raw data.

> I suppose one could look for stationary segments of the signal, and then
> attempt to compare spectra across multiple 'stationary looking'
> segments; if the signal really is stationary, i'd expect no changes
> right? I have 45,000 data points in a single time-series.

See detrending.

Cheers

Jason
-- 
Indigo Industrial Controls Ltd.
http://www.indigoindustrial.co.nz
+64-(0)21-343-545




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