On Tue, 1 Sep 2009, Oliver Heering wrote:
coming from data mining and machine learning i have the
following question which may or may not sound stupid for you,
but as i have no clue and there are no stupid questions (just
stupid answers) i take my chance posting the question to this
I already figured out how to fit an ARIMA process to my data
(ok, how to actually find GOOD AR/MA/difference orders would be
another question). And i can save the resulting model as Icon to
my session. Does that mean i can also apply my model to any
other data (of the same type of course)? Let's say i fit my
model against a timeseries range A. How can i find out how my
model fits to another timeseries range B?
Maybe this isn't possible at all and i am misunderstanding the
whole concept of ARIMA models and -forecasting. I am primary
working with the data mining tool "RapidMiner", which allows you
to easily apply any learned model to new and unseen data
(classification mainly, but regression as well) and i wonder if
it is the same with an estimated ARIMA model.
In the gretl GUI, an existing model is in effect defined by an
estimation method (e.g. ARIMA). On opening a saved model you can
use the menu item "/Edit/Modify model..." in the model window to
create a new, related model. You can substitute a new dependent
variable, and/or edit the list of regressors, and/or tweak things
such as the lag order.
In a gretl script, there's no limit to the extent to which you can
use an existing model as the basis for a new one. Just copy,
paste and edit.
By the way, i appreciate that case markers can now be up to 15
chars in length, but what are they actually used for? Of course
i'd like to have them show up on my plots, which i currently do
by manually tweaking the gnuplot commands, but i think this
functionality is still missing in gretl, am i correct? So what's
left? Is there any practical use-case for case markers in
With time-series data there's little or no use for case markers,
since the general assumption is that date strings should be used.
Case markers are most useful for cross-sectional data where
otherwise the observations would be anonymous.