[R] Interpolating / smoothing missing time series data

[Francisco J. Zagmutt]

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


>From: Gabor Grothendieck <ggrothendieck at gmail.com>
>Reply-To: ggrothendieck at gmail.com
>To: David James <djames at frontierassoc.com>
>CC: r-help at stat.math.ethz.ch
>Subject: Re: [R] Interpolating / smoothing missing time series data
>Date: Wed, 7 Sep 2005 22:19:17 -0400
>
>On 9/7/05, David James <djames at frontierassoc.com> wrote:
> > The purpose of this email is to ask for pre-built procedures or
> > techniques for smoothing and interpolating missing time series data.
> >
> > I've made some headway on my problem in my spare time.  I started
> > with an irregular time series with lots of missing data.  It even had
> > duplicated data.  Thanks to zoo, I've cleaned that up -- now I have a
> > regular time series with lots of NA's.
> >
> > I want to use a regression model (i.e. ARIMA) to ill in the gaps.  I
> > am certainly open to other suggestions, especially if they are easy
> > to implement.
> >
> > My specific questions:
> > 1.  Presumably, once I get ARIMA working, I still have the problem of
> > predicting the past missing values -- I've only seen examples of
> > predicting into the future.
> > 2.  When predicting the past (backcasting), I also want to take
> > reasonable steps to make the data look smooth.
> >
> > I guess I'm looking for a really good example in a textbook or white
> > paper (or just an R guru with some experience in this area) that can
> > offer some guidance.
> >
> > Venables and Ripley was a great start (Modern Applied Statistics with
> > S).  I really had hoped that the "Seasonal ARIMA Models" section on
> > page 405 would help.  It was helpful, but only to a point.  I have a
> > hunch (based on me crashing arima numerous times -- maybe I'm just
> > new to this and doing things that are unreasonable?) that using
> > hourly data just does not mesh well with the seasonal arima code?
>
>Not sure if this answers your question but if you are looking for something
>simple then na.approx in the zoo package will linearly interpolate for you.
>
> > z <- zoo(c(1,2,NA,4,5))
> > na.approx(z)
>1 2 3 4 5
>1 2 3 4 5
>
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