R has such a function (called auto.arima in forecast package):
auto.arima Fit best ARIMA model to univariate time series
On 2011.09.28 03:06, Allin Cottrell wrote:
On Mon, 26 Sep 2011, Jeevan Tambuluri wrote:
> I have time-series data with a large number of samples (anywhere
> from 180 to 360 daily samples). I would like to use GRETL to
> automatically pick a ARIMA model to forecast values for as many as
> 30-60 days into future. I would like to also model for seasonality
> in the data.
> I have been playing with gretl tool to explore my data with
> various ARMIA models - like ARIMA (1,0,1), ARIMA (1,1,1) and it
> has been a great experience so far.
> Now, here is my issue. I would like to write a library to
> basically take time series data as input, try various ARIMA models
> and pick the most appropriate one "automatically" without human
> input or intervention. I understand this is fairly novice way to
> looking at the forecasting, but my application's use-case is
> simple enough to do it this way.
> Now, has anyone tried this using GRETL where multiple models are
> tried and results (fstats output) are compared to pick one over
> the other? If so is there a GRETL script out there that can be
I'm not aware of any such script, although that doesn't mean that
such a script doesn't exist somewhere.
However, my understanding is that TRAMO -- which is available in a
form compatible with gretl, see for example
-- does this sort of thing
(automatic ARIMA model selection). But (I may be wrong) TRAMO
probably does not handle intra-day "seasonality"; I think it's
mostly oriented to monthly or quarterly data.
Maybe Ignacio Diaz-Emparanza, our resident expert on ARIMA models,
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