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 -- Help protect Wikipedia. Donate now: http://wikimediafoundation.org/wiki/Support_Wikipedia/en [[alternative HTML version deleted]] ______________________________________________ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.