[R] Forecasting with R/Need Help. Steps shown below with the imaginary data

[Phil Spector]

Ruchi -
    If the only way you can figure out to read your data
into R is through SAS, I think you need to spend more time
with the R introductory documentation, for example

   http://cran.r-project.org/doc/manuals/R-intro.html

While data is usually read from a file, you can imitate
SAS' datalines command using the textConnection function:

> dat = read.table(textConnection('2008 12 13 12 14 13 12 11 15 10 12 12 12
+ 2009 12 13 12 14 13 12 11 15 10 12 12 12
+ '))

Now convert the data into a time series.  We can ignore the first
column of dat, and use the transpose because R stores its data by
columns:

> myts = ts(as.vector(t(dat[,-1])),start=c(2008,1),frequency=12)

Now, we can load the forecast package, which contains auto.arima:

> library(forecast)

>From here, things should work as you expect:

> fit = auto.arima(myts)
> fcast = forecast(fit)
> plot(fcast)
> summary(fcast)
 					- Phil Spector
 					 Statistical Computing Facility
 					 Department of Statistics
 					 UC Berkeley
 					 spector at stat.berkeley.edu



On Thu, 7 Oct 2010, Vangani, Ruchi wrote:

> 1.  This is an imaginary data on monthly outcomes of 2 years and I want to forecast the outcome for next 12 months of next year.
>
>
> data Data1;
> input Yr Jan Feb Mar Apr May June July Aug Sept Oct Nov Dec;
> datalines;
> 2008 12 13 12 14 13 12 11 15 10 12 12 12
> 2009 12 13 12 14 13 12 11 15 10 12 12 12
> ;
> run;
>
> I converted the above data into the below format to use it in R as it was giving error: asking to use only univariate time series.
>
> data Data1;
> input RR;
> datalines;
> 12
> 14
> 17
> 15
> 13
> 15
> 15
> 14
> 15
> 14
> 16
> 15
> 15
> 18
> 16
> 16
> 15
> 14
> 15
> 16
> 16
> 14
> 13
> 12
> ;
> run;
>
>
> 1.  I successfully took this data thru xport into R using the below codes:
>
> libname xportout xport 'H:\Care Transition Evaluation\CT-Codes\SAS\gross up\Data\Forc1.xpt';
>
> data xportout.Forc1;
>   set Data1;
> run;
>
>
>
> setwd("H:/Care Transition Evaluation/CT-Codes/SAS/gross up/Data")
> getwd()
> Forc<-read.xport("Forc1.xpt")
> attach(Forc)
> names(Forc)
> Forc
>
>
> 1.  Used the auto.arima codes:
> fit <- auto.arima(Forc)
> fcast <-forecast(fit)
> plot(fcast)
> summary(fcast)
>
> But the following error comes on using the first line of code:
> fit <- auto.arima(Forc)
> -----------------------------------------------------------------------
> Error in model.frame.default(formula = x ~ 1, drop.unused.levels = TRUE) :
>  invalid type (list) for variable 'x'
> -----------------------------------------------------------------------
>
>
> 1.  Further:
> I tried to use the Holt Winters Algorithm using the below codes:
>
> Final <- HoltWinters(Forc,gamma=FALSE)
> plot(forecast(Final))
> Final
>
> pred <- predict(Final, n.ahead = 8)
> plot(Final, predicted.values = pred)
>
> pred
>
> These codes work completely fine but it adjusts for the trend and does not takes into account the seasonal component which is more important in the analysis.
>
>
>
> Please help.
>
> Thanks,
> Ruchi
>
>
>
>
>
>
>
> The information contained in this communication is highl...{{dropped:18}}
>
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