if your two points are valid and you would be willing to run a least
spuares  regression on then you should be fairly comfortable with
using na.approx in the zoo package.  If interpolating values is going
to give you an erroneous result then maybe this is not the correct
path.  But have a look.

On Mon, Sep 1, 2008 at 1:50 AM,  <[EMAIL PROTECTED]> wrote:
> I just realized after some tips and a little digging that what I was trying 
> to do "manually" has already been done. I was trying to fit my data using 
> 'lm' then taking the "residual" data and trying to do a spectral estimate 
> (for seasonality) usiing fft and then passing the "residual" of all of that 
> to arima to get the irregular portion of the time series forecast equation. I 
> found 'stl' that advertises that it does all of this (and probably better 
> than my efforts). The only problem that I found was that it takes a 
> time-series object. Time-series objects don't handle missing observations. 
> Say I only have two observations for the year (enough to fit a line through). 
> I can easily fit a line through the observation points but if I add in zeros 
> for the missing observations least squares will undoubtedly throw my 
> observations out and fit the "wrong" line. The other steps will more than 
> likely have residual data so I don't have to worry for these steps about 
> missing data. Has any!
 on!
>  e used 'stl' with missing observations? If so what sort of tricks did you 
> use to get around the missing data and time series?
>
> Thank you.
>
> Kevin
>
> ______________________________________________
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>



-- 
Stephen Sefick
Research Scientist
Southeastern Natural Sciences Academy

Let's not spend our time and resources thinking about things that are
so little or so large that all they really do for us is puff us up and
make us feel like gods. We are mammals, and have not exhausted the
annoying little problems of being mammals.

        -K. Mullis

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