On Thu, 8 May 2008, Daniele Amberti wrote:
> 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/
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