Understanding forecasting in R I am presently trying to learn R.  I would like to be able to apply it more in my work environment as I am an analyst in the Health Care industry.  I am presently trying to use R to forecast.  What is the best forecasting package in R?  
I am presently using the forecast package.  I have tried to fit the ets models to my data but I feel that it is giving me some fairly unreasonable solutions.  The data is flat, meaning that it does not linearly increase and there are some fluctuations, but I have not been able to assess whether or not those fluctuations are seasonal.  I am assuming they are not. 
How can I calculate the out of sample error when I am comparing forecasting models?  Also, is there a way to plot my forecasted data against the actual values?  Lastly, how can I determine the model that is generated from the forecast?  
Thanks for all of your help in advance.
 A: I believe 'forecast' is a very good choice, but it surely depends on the tasks you want to complete with the package. The main author Rob Hyndman is co-authoring an open book on forecasting you might have a look at in order to find out more about applying forecast: http://otexts.com/fpp/ There's a link to some talk on forecasting with R on his blog as well: http://robjhyndman.com/talks/melbournerug/ And although you might have stumbled upon it, the Hyndman/Khandakar paper provides quite some insight on the forecast package along with example code: http://robjhyndman.com/papers/automatic-forecasting/ Else, you might want to check the documentation, it has short examples for most commands in addition to explanations. 
@"The data is flat meaning, it does not linearly increase and there are some fluctuations, but I have not been able to assess whether or not those fluctuations are seasonal.": Maybe try some seasonal decomposition tool like "stl" and see http://otexts.com/fpp/6/
A: There is no best forecasting package in R. For one thing, R is open source so there are often multiple packages that do similar things and you can choose which one seems to be the most up-to-date and works to your taste.
More importantly, though, is: how you forecast depends on how you model. Do you have a univariate time series and nothing but that data? Or do you have a univariate time series with several predictor variables? Do you have a multivariate time series? Are you even using a time series at all? ("Forecasting" implies time series, but that's not necessarily the case.)
So the first question would probably be: what data do you have, and what kind of model are you trying to make? Modeling options and forecasting options will then be easier.
