On Sat, Dec 25, 2010 at 8:08 AM, analys...@hotmail.com
<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?
>
This isn't quite what you asked but it seems more suitable to what you
need. Instead of using long form data we transform it to wide form
with one client per column. Try copying this from this post and
pasting it into your R session:
Lines <- "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"
library(zoo)
library(chron)
# read in. split = 1 converts to wide form
# can use "myfile.dat" in place of textConnection(Lines) for real data
z <- read.zoo(textConnection(Lines), split = 1, index = 2, FUN = chron,
na.strings = "missing")
# d is matrix with one row per date and one col per client
d <- coredata(z)
# just use last point as our forecast for next 3 dates
naive.forecast <- function(x) rep(tail(x, 1), 3)
pred <- apply(d, 2, naive.forecast)
# put predictions together with the data
rbind(d, pred)
For the data you showed this gives:
> rbind(d, pred)
121212 323232
[1,] NA 22
[2,] NA 0
[3,] NA NA
[4,] 32 NA
[5,] 15 NA
[6,] 15 NA
[7,] 15 NA
[8,] 15 NA
--
Statistics & Software Consulting
GKX Group, GKX Associates Inc.
tel: 1-877-GKX-GROUP
email: ggrothendieck at gmail.com
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