Interpolating / smoothing missing time series data

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?

z <- zoo(c(1,2,NA,4,5))
na.approx(z)