Surely, this sounds like a bug in the optim function.
The rule of thumb with ts data is to scale so that data have mean 0 and
unit variance and then fit
a) for non-seasonal data the IMA model (0,1,1); and
b) for seasonal data so-called Airline Model (0,1,1)X(0,1,1)S
see for example A course in TS analysis by Daniel Pena.
These can usually be fit to most series unless they are very irregular
(e.g. mainly zeros) and then Box-Jenkins ARIMA is inappropriate.
The fit of any other model is then judged against these two models. If a
proposed model fit fails it is usual report the fault and then to step over
that model and try the next in sequence - optim should do this, if not then
it's a code fault.
Very often with nasty series only the 2 basic models can be fit and these
results are reported - but for these the parameters may not even fully
converge.
The moral of all this is that automatic modelling methods are handy but not
a substitute for looking at the acf and choosing an appropriate
parameterisation.
Can someone say whether optim has automatic outlier detection - this is a
must have with this type of procedure.
Gerard
nas...@uottawa.ca
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27/01/2009 15:45 Subject
Re: [R] optim() and ARIMA
dhabby wrote:
Last week I run in to a lot a problems triyng to fit an ARIMA model to
a
time series. The problem is that the internal process of the arima
function
call function "optim" to estimate the model parameters, so far so
good...
but my data presents a problem with the default method "BFGS" of the
optim
function, the output error looks like this:
Error en optim(init[mask], armafn, method = "BFGS", hessian = TRUE, control
= optim.control, :
non-finite finite-difference value [7]
I don't know much about the calls from ARIMA to optim, but when I modified
Fletcher's 1970 VM method (called BFGS in R), I was aiming to make it
extremely robust to messy functions. (I had machinery with only 6 hex
digits for floating point and no guard digits. Furthermore, only 8K bytes
for program AND data. This was 1975/6.) However, BFGS needs gradients, and
it is clear there's some sort of trouble approximating them with finite
differences.
Changing optimization methods might succeed on this occasion, but your
problem is more likely bad scaling or some setup where your ARIMA model
essentially is not sensible. Can you duplicate the message on a small
example problem?
John Nash
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