Interpolating / smoothing missing time series data

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)

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