Stefano,
I see you already have an answer that works for you.
Sometimes you want to step back and see if some modification makes a problem
easier to solve.
I often simply switch to using tools in the tidyverse such as dplyr for parts
of the job albeit much of the same can be done using functio
Às 18:54 de 31/08/2024, Christofer Bogaso escreveu:
Hi,
I have run following code to obtain one step ahead confidence interval
from am arima model
library(forecast)
set.seed(100)
forecast(Arima(rnorm(100), order = c(1,0,1), xreg = rt(100, 1)), h =
1, xreg = 10)
However this appear to provide
Yes, very helpful.
Also, my second question was how can I extract the prediction standard error?
On Sun, Sep 1, 2024 at 1:00 AM Mark Leeds wrote:
>
> Chris: As David mentioned, if you have "new" data, then the interval has to
> be a prediction
> interval because the difference between a CI and
Chris: As David mentioned, if you have "new" data, then the interval has to
be a prediction
interval because the difference between a CI and a PI is that the PI is
constructed for data
that hasn't been seen yet. The CI is constructed for data that's already
there. I hope this helps.
On Sat, Aug 3
I want to obtain confidence interval for a new data as well as
estimate of SE for the new data
On Sat, Aug 31, 2024 at 11:58 PM David Winsemius wrote:
>
>
> Sent from my iPhone
>
> > On Aug 31, 2024, at 10:55 AM, Christofer Bogaso
> > wrote:
> >
> > Hi,
> >
> > I have run following code to obt
Sent from my iPhone
> On Aug 31, 2024, at 10:55 AM, Christofer Bogaso
> wrote:
>
> Hi,
>
> I have run following code to obtain one step ahead confidence interval
> from am arima model
>
> library(forecast)
>
> set.seed(100)
>
> forecast(Arima(rnorm(100), order = c(1,0,1), xreg = rt(100,
Hi,
I have run following code to obtain one step ahead confidence interval
from am arima model
library(forecast)
set.seed(100)
forecast(Arima(rnorm(100), order = c(1,0,1), xreg = rt(100, 1)), h =
1, xreg = 10)
However this appear to provide the Prediction interval, however I
wanted to get the
Thank you for all your suggestions.
Ivan's hint has been very easy to implement, a good solution indeed.
Thank you again
Stefano
(oo)
--oOO--( )--OOo--
Stefano Sofia PhD
Civil Protection - Marche Region - Italy
Meteo Section
Snow Section
Via del Co
Às 12:15 de 31/08/2024, Stefano Sofia escreveu:
Dear R-list users,
I deal with semi-hourly data from automatic meteorological stations.
They have to pass a manual validation; suppose that status = "C" stands for correct and
status = "D" for discarded.
Here a simple example with "Snow height"
В Sat, 31 Aug 2024 11:15:10 +
Stefano Sofia пишет:
> Evaluating the daily mean indipendently from the status is very easy:
>
> aggregate(mydf$hs, by=list(format(mydf$data_POSIX, "%Y"),
> format(mydf$data_POSIX, "%m"), format(mydf$data_POSIX, "%d")),
> my.mean)
>
>
> Things become more comp
Dear R-list users,
I deal with semi-hourly data from automatic meteorological stations.
They have to pass a manual validation; suppose that status = "C" stands for
correct and status = "D" for discarded.
Here a simple example with "Snow height" (HS):
mydf <- data.frame(data_POSIX=seq(as.POSIX
11 matches
Mail list logo
