# Re: [R] ARIMA, AR, STEP

From: Prof Brian Ripley <ripley_at_stats.ox.ac.uk>
Date: Thu, 08 May 2008 13:47:40 +0100 (BST)

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)
>
> 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

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Received on Thu 08 May 2008 - 12:59:33 GMT

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