ARIMA, AR, STEP - [ ] Message is from an unknown sender

I agree with You that I'm trying to estimate structure that is not there, it's just an example, anyway I mean like function ar {stats} not arima0(). Function ar() is very simple with an automatic selection criterion (AIC etc). but miss moving average, integration, seasonality and predict method.

Library forecasting seems to do something this way but documentation is not so exhaustive.

Thanks

-----Original Message-----
From: Prof Brian Ripley [mailto:ripley at stats.ox.ac.uk]
Sent: gioved? 8 maggio 2008 14.48
To: Daniele Amberti
Cc: r-help at r-project.org
Subject: Re: [R] ARIMA, AR, STEP - [ ] Message is from an unknown sender

Here is my problem:
Autoregressive models are very interesting in forecasting consumptions (eg water, gas etc).

Generally time series of this type have a long history with relatively simple patterns and can be useful to add external regressors for calendar events (holydays, vacations etc).

arima() is a very powerful function but kalman filter is very slow (and I foun difficulties of estimation) while ar() is too simple but fast (but do not have a method for forecasting I think)

Is there something like arima() but entirely implemented in C and efficient like ar() ???

You mean, like arima0()?

I am not sure arima() is inefficient, rather that you are asking for the
solution to a computationally difficult problem (which in your example is
looking to estimate structure that is not there!).

Is there something like step() for ARIMAX? It would be very useful for external regressors.

Try the code below (imagine daily data for some years):

x <- rep(c(15,20,20,20,20,12,10), 5*52)
set.seed(1234)
x <- x + rnorm(length(x))

#plot(as.ts(x[1:21]))

#slow
arima(x, c(1,0,1), list(order = c(2,0,0), period = 7))
arima(x, c(2,0,0), list(order = c(3,0,0), period = 7))
#slower
arima(x, c(2,0,1), list(order = c(3,0,0), period = 7))
# do not converge
arima(x, c(2,0,0), list(order = c(3,0,1), period = 7))

#fast but not enough sophisticated
ar(x)

Thanks in advance
Daniele

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--
Brian D. Ripley,                  ripley at stats.ox.ac.uk
Professor of Applied Statistics,  http://www.stats.ox.ac.uk/~ripley/
University of Oxford,             Tel:  +44 1865 272861 (self)
1 South Parks Road,                     +44 1865 272866 (PA)
Oxford OX1 3TG, UK                Fax:  +44 1865 272595

ORS Srl

Via Agostino Morando 1/3 12060 Roddi (Cn) - Italy
Tel. +39 0173 620211
Fax. +39 0173 620299 / +39 0173 433111
Web Site www.ors.it

------------------------------------------------------------------------------------------------------------------------
Qualsiasi utilizzo non autorizzato del presente messaggio e dei suoi allegati ? vietato e potrebbe costituire reato.
Se lei avesse ricevuto erroneamente questo messaggio, Le saremmo grati se provvedesse alla distruzione dello stesso
e degli eventuali allegati.
Opinioni, conclusioni o altre informazioni riportate nella e-mail, che non siano relative alle attivit? e/o
alla missione aziendale di O.R.S. Srl si intendono non  attribuibili alla societ? stessa, n? la impegnano in alcun modo.