[R] Interpolating / smoothing missing time series data

[Milos Zarkovic]

I agree that excessive interpolation might cause problems.
Maybe  Lomb-Scargle periodogram could be used (spectral analysis on unevenly 
spaced data).
Another option would be to use Kalman filtering to interpolate data.
I belive that both are implemented in R.

Milos Zarkovic
----- Original Message ----- 
From: "Thomas Petzoldt" <thpe at hhbio.wasser.tu-dresden.de>
To: "Francisco J. Zagmutt" <gerifalte28 at hotmail.com>
Cc: <r-help at stat.math.ethz.ch>
Sent: Thursday, September 08, 2005 8:17 AM
Subject: Re: [R] Interpolating / smoothing missing time series data


> Francisco J. Zagmutt wrote:
>> I don't have much experience in the subject but it seems that 
>> library(akima)
>> should be useful for your problem. Try library(help="akima") to see a 
>> list
>> of the functions available in the library.
>>
>> I hope this helps
>>
>> Francisco
>
> Yes, function aspline() of package akima is well suited for such things:
> no wiggles like in spline() and less variance reducing than approx().
> But in any case: excessive interpolation will definitely lead to biased
> results, in particular artificial autocorrelations.
>
> If ever possible, David should look for methods, capable of dealing with
> missing data directly.
>
> Thomas P.
>
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