Sorry,
but this gives me the shivers!
Are all your time series linear?
For each model you should check the residuals and their squares to see if
they are uncorrelated (Box-ljung Chi-sq).
Another useful check is to test for a trend in the coefficient of variation
of the residuals.
If the series is
Evan DeCorte wrote:
Thanks for the great feedback. Conceptually I understand how you would go about testing out of sample performance. It seems like accuracy() would be the best way to test out of forecast performance and will help to automate the construction of statistics I would have calculated
Thanks for the great feedback. Conceptually I understand how you would go about
testing out of sample performance. It seems like accuracy() would be the best
way to test out of forecast performance and will help to automate the
construction of statistics I would have calculated on my own.
Howe
> for(i in 1:length(data))
> {
> point_data = unlist(data[i], use.names = FALSE)
> x = auto.arima(point_data , max.p = 10, max.q = 10, max.P = 0, max.Q =
> 0, approximation = TRUE)
>
> }
>
> However, I would like to find a way to test the out of sample predictive
> power of these mod
Evan DeCorte wrote:
I am trying to make estimates of the predictive power of ARIMA models estimated by the auto.arima() function.
I am looping through a large number of time seiries and fitting ARIMA models with the following code.
data1 <- read.csv(file = "case.csv", header = T)
data <- data
I am trying to make estimates of the predictive power of ARIMA models estimated
by the auto.arima() function.
I am looping through a large number of time seiries and fitting ARIMA models
with the following code.
data1 <- read.csv(file = "case.csv", header = T)
data <- data1
output = c(1:leng
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