ideas, modeling highly discrete time-series data

Hello all,

? ?First of all, thanks so those of you who helped me a week or so ago
managing a time series with varying gaps between the data series in 'R'.
(My final preferred solution was to use "its" function & then
forecast(Arima( ) ). ?)

? ?My next question is a general statistical question where I'd like some
advice, for those willing / able to proffer any wisdom:

? - I need to predict using this same time series, where the *data* are
? highly discrete. ?E.g., I will have values like 1e5, 2.2e5, and 3.6e5, but I
? will never have 1.3e5 or 1.8e5, etc.
? ? ?- I could simply leave these values as discrete, similar to a binomial
? ? ?distribution, but then I am not sure how to use time series tricks like
? ? ?arima above. ?For time-series analyses that I know of, an
assumption of an
? ? ?approximately normal distribution is expected. ?No simple normalization
? ? ?(e.g., log(values) ) works, since the non-normality arises from
the highly
? ? ?discrete distribution more than any drastic asymmetry in the population
? ? ?spread.
? ? ?- I could leave the values as they are an work with a model where the
? ? ?assumption is violated... I am not sure how sensitive a model
such as arima
? ? ?is on the population distribution
? ? ?- Or I could... (here's where I am hoping for some collective genius).

? ?Thanks in advance for any help! ?If this isn't the best forum, since I
know this is not specifically an 'R' question, please let me know of a
better forum to post such a question.

? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ?Thanks!
? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? Mike



"Telescopes and bathyscaphes and sonar probes of Scottish lakes,
Tacoma Narrows bridge collapse explained with abstract phase-space maps,
Some x-ray slides, a music score, Minard's Napoleanic war:
The most exciting frontier is charting what's already here."
?-- xkcd

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