[R] Handling nonexistent observations in R for time series analysis and forecasting
bgunter.4567 at gmail.com
Mon Mar 27 16:33:29 CEST 2017
A statistics, not really an R programming question, so I believe OT here.
1. See the CRAN Time series task view for what's available:
2. stats.stackexchange.com is a good site for statistical questions.
"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 Mon, Mar 27, 2017 at 7:15 AM, Paul Bernal <paulbernal07 at gmail.com> wrote:
> Dear friends,
> Hope you are all doing great. I am trying to model historical data on
> transits, and the dates are in the following format: 1985-10-01
> 00:00:00.000 (this would be october, 1985).
> The data comes from an SQL Server Database and there are several missing
> observations. The problem is that, for example, there are dates for which
> no transit was recorded (because no transit took place) and instead of
> having that date recorded with an NA value, that date does not appear,
> resulting in a sequence like this:
> 1985-01-01 00:00:00.000, 1985-02-01 00:00:00.000, 1985-05-01 00:00:00.00
> in this example you start in january 1985, the february 1985, then the next
> available observation is on may 1985.
> I know R´s tsclean(data) function takes care of missing values, but that
> only works if you at least have the non available dates recorded with a
> value of NA, but what if I do not have those missing observations?
> Any help will be greatly appreciated,
> Best regards,
> [[alternative HTML version deleted]]
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