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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