R 1.7.0 on Windows 2000
I try to estimate the 'best' ARIMA model for a time series (TS). The only
way I know to solve this problem is by brute force. Within 6 nested for
loops I try to get the best model based upon the Aikake Information
Criterion(aic).
A summary of the code:
best.model <- arima(TS, order = c(1, 0, 0), seasonal = list(order = c(0, 0,
0), period = frequency(TS)) )
#And by brute force
p, q, r, s are nested from 0 to 3 and i and j are nested from 0 to 2. p and
q are not both allowed to be 0.
# determine the best series
tmp <- arima(TS, order = c(p, i, q), seasonal = list(order = c(r,
j, s), period = frequency(TS)) )
if(best.model$aic > tmp$aic)
{
best.model <- temp
}
Result:
For a time series of 200 values it takes about 75 minutes to come up with
an answer and 50 warnings of the types:
- NaNs produced in: log(x)
- possible convergence problem: optim gave code= 1 in: arima(series, order
= c(p, i, q), seasonal = list(order = c(r, ...
Questions
1. Is there a way to speed up the process (beside using a faster computer)?
2. How can I prevent the warnings from occurring occurring?
Greetings,
Wouter
Wouter van Rijsinge
wprijsin@cs.uu.nl
Student Medical Technical Computer Science at the University of Utrecht,
Holland.
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