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New post for Rhelp

Edoardo Silvestri
I have a database based on hourly data and I need to forecast next 24h of a
single variable. I was thinking about applying an ARIMA model with some
exogenous variables but I don't succeed to configure the hourly frequency,
estimate ARIMA parameters, pdq ( exists some tests to check which
parameters are better for the model?) and the structure of the model in its
easy form because I would also like to introduce some seasonality form by
analyzing some variables I highlighted some daily and weekly behaviours
similar.



I recognize that it could be quite difficult the problem but if you have
also some useful links or some codes that can help me, please send me.

        [[alternative HTML version deleted]]

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Re: New post for Rhelp

Bert Gunter-2
Statistics issues are generally off topic here; and we generally prefer
posters to show us their own efforts rather than expecting us to solve the
problem for them.

However, this CRAN time series task view may be useful to you:

https://cran.r-project.org/web/views/TimeSeries.html

Cheers,
Bert




Bert Gunter

"The trouble with having an open mind is that people keep coming along and
sticking things into it."
-- Opus (aka Berkeley Breathed in his "Bloom County" comic strip )

On Wed, Aug 1, 2018 at 8:40 AM, Edoardo Silvestri <
[hidden email]> wrote:

> I have a database based on hourly data and I need to forecast next 24h of a
> single variable. I was thinking about applying an ARIMA model with some
> exogenous variables but I don't succeed to configure the hourly frequency,
> estimate ARIMA parameters, pdq ( exists some tests to check which
> parameters are better for the model?) and the structure of the model in its
> easy form because I would also like to introduce some seasonality form by
> analyzing some variables I highlighted some daily and weekly behaviours
> similar.
>
>
>
> I recognize that it could be quite difficult the problem but if you have
> also some useful links or some codes that can help me, please send me.
>
>         [[alternative HTML version deleted]]
>
> ______________________________________________
> [hidden email] mailing list -- To UNSUBSCRIBE and more, see
> https://stat.ethz.ch/mailman/listinfo/r-help
> PLEASE do read the posting guide http://www.R-project.org/
> posting-guide.html
> and provide commented, minimal, self-contained, reproducible code.
>

        [[alternative HTML version deleted]]

______________________________________________
[hidden email] mailing list -- To UNSUBSCRIBE and more, see
https://stat.ethz.ch/mailman/listinfo/r-help
PLEASE do read the posting guide http://www.R-project.org/posting-guide.html
and provide commented, minimal, self-contained, reproducible code.
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Re: New post for Rhelp

JohnDee
In reply to this post by Edoardo Silvestri
On Wed, 1 Aug 2018 17:40:54 +0200
Edoardo Silvestri <[hidden email]> wrote:

> I have a database based on hourly data and I need to forecast next
> 24h of a single variable. I was thinking about applying an ARIMA
> model with some exogenous variables but I don't succeed to configure
> the hourly frequency, estimate ARIMA parameters, pdq ( exists some
> tests to check which parameters are better for the model?) and the
> structure of the model in its easy form because I would also like to
> introduce some seasonality form by analyzing some variables I
> highlighted some daily and weekly behaviours similar.
>
>
>
> I recognize that it could be quite difficult the problem but if you
> have also some useful links or some codes that can help me, please
> send me.
>
You are talking about analyzing data in a regular time series.  R has
wide ranging time series analysis packages.  I would suggest starting
with the basic package that comes with an R download and reading the
basic information associated with it.  You could also checkout Venables
and Ripley, _Modern Applied Statistics with S [which R is a dialect
of].  If this is a homework question, hit the books.

JWDougherty

______________________________________________
[hidden email] mailing list -- To UNSUBSCRIBE and more, see
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PLEASE do read the posting guide http://www.R-project.org/posting-guide.html
and provide commented, minimal, self-contained, reproducible code.