[R] ideas, modeling highly discrete time-series data

[Kjetil Halvorsen]

You could try the timeseries list at
https://www.jiscmail.ac.uk/cgi-bin/webadmin?A0=TIMESERIES

kjetil

On Mon, Dec 20, 2010 at 6:26 PM, Mike Williamson <this.is.mvw at gmail.com> wrote:
> 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
>
>
>
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>
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