[R-SIG-Finance] Solicitation of opinions on which Timeseries object(s) to utilize.

[Dirk Eddelbuettel]

On 20 January 2008 at 09:55, Joe W. Byers wrote:
| I am soliciting your opinions on the different time series objects as to 
| which you prefer, or which you do not prefer.  I have been using 
| dataframes and date columns to handle my timeseries processes for awhile 
| now, and recently began experimenting with different timeseries 
| packages, mainly: zoo, ts, tseries, Rmetric's timeSequences, and 
| rseries.  There are two reasons for my change here
| 1.  Utilize the object oriented components of these packages,
| 2.  Standardization for handling the nuances of my time series data.

For 'financial data' in the large sense I have been using zoo for years,
after having used its. I have also used ts for some ultra-low-frequency
macro-economic series, but ts becomes useless once you leave montly or
quarterly frequencies and their fixed delta-t. I have basically no experience
with the others.

zoo is good as it does all the merging, subsetting, plotting, indexing,
converting to and from (ie I often 'drop' to data.frame(coredata(zooobject))
for analysis) I need.  Working with high-frequency data, I occassionally hit
the constraint of 'has to have unique indices' in which case I just pad a
micro-second each to the occassional identical timestamp. That's a small
price to pay. 

You did not mention xts, which is the newest member of the clan. It extends
zoo and allows 'annotations', ie you can set attributes about the when /
where / how / ... of your data. Xts also overcomes the uniqueness constraint
for the index (and I am not sure how they do it) and offers richer 'logical'
subsetting.  It looks like a winner, and this may just what you need. Have a
look at xts.  Usual caveat: Haven't used it myself yet...

Hope this helps, Dirk

-- 
Three out of two people have difficulties with fractions.