On Dec 25, 2010, at 10:45 AM, analys...@hotmail.com wrote:
On Dec 25, 10:17 am, David Winsemius <dwinsem...@comcast.net> wrote:
On Dec 25, 2010, at 8:08 AM, analys...@hotmail.com wrote:
I have a data frame that reads
client ID date transcations
323232 11/1/2010 22
323232 11/2/2010 0
323232 11/3/2010 missing
121212 11/10/2010 32
121212 11/11/2010 15
.................................
I want to order the rows by client ID and date and using a black-box
forecasting method create the data fcst(client,date of forecast,
date
for which forecast applies).
Assume that I have a function that given a time series
x(1),x(2),....x(k) will generate f(i,j) where f(i,j) = forecast j
days
ahead, given data till date i.
How can the forecast data be best stored and how would I go about
the
taks of processing all the clients and dates?
http://lmgtfy.com/?q=forecast+r-project
--
David Winsemius, MD
West Hartford, CT
Thanks. I am planning to write my own univariate forecasting routine.
My question is mostly concerned with separting out the time series by
client,
See the various manipulation functions: split, aggregate, tapply, and
the plyr package. Specifics will depend on the data structures that
constitute input.
generating the forecasts
Well, there is the forecast package ... but you said you had methods
in mind, so you can offer code.
and then putting everything back
Will depend on the choices made in the first step.
together into something like
ClientID | forecast date| date forecast is for |forecast| actual
The answer is going to depend on the data structures used. Show us
some data _and_ your code.
--
David Winsemius, MD
West Hartford, CT
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