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() ???
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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