On Mon, Sep 12, 2011 at 4:42 AM, Eran Eidinger wrote:
> Hello,
>
> I have a time-series that has some missing samples.
> I was thinking on completing them using either zero-order hold or linear
> interpolation.
> I am looking for an efiicient way (other than a loop...) of identifiying the
> missin
Hello Michael, Joshua,
Thanks alot for your replies, this really helps.
As to my main problem - I am tryin to identify a cyclic behavior in a
time-series using FFT, and I am struggiling with missing samples.
It is not trivial that linear interpolation is the answer (it probably is
not), but right
On Sep 17, 2011, at 5:04 PM, Joshua Wiley wrote:
On Sat, Sep 17, 2011 at 1:43 PM, Joshua Wiley
wrote:
Hi Eran,
You have already gotten some suggestions from Michael, but I think
that Rich is correct to question the rational.
I hope we should question the rationale, and doubt that Rich has
On Sat, Sep 17, 2011 at 1:43 PM, Joshua Wiley wrote:
> Hi Eran,
>
> You have already gotten some suggestions from Michael, but I think
> that Rich is correct to question the rational. Any mechanism you
> choose to replace the missing values will impose its structure on the
> data. Veritate ab abs
Hi Eran,
You have already gotten some suggestions from Michael, but I think
that Rich is correct to question the rational. Any mechanism you
choose to replace the missing values will impose its structure on the
data. Veritate ab absurdo:
## data
x <- sin(seq(1, 17, .1)) + seq(-.5, .5, length.out
On Sat, 17 Sep 2011, R. Michael Weylandt wrote:
How are your time samples missing?
Michael/Eran,
Pardon me for jumping in since I did not read the original message, but I
have a more fundamental question: are the missing time samples meaningful?
For example, a time series of precipitatio
How are your time samples missing?
If they are recorded as NA, the na.locf() function will fill them with the
previous value (zero-order hold) and with the reversability arguments can
give linear interpolation:
library(xts)
x = c(1:5,NA,6:10)
x = xts(x,Sys.Date()+0:10)
na.locf(x)
(na.locf(x) + n
Hello,
I have a time-series that has some missing samples.
I was thinking on completing them using either zero-order hold or linear
interpolation.
I am looking for an efiicient way (other than a loop...) of identifiying the
missing time slots and filling them.
Can you think of any methods that mi
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