On Thu, 3 Apr 2014, Roy Mendelssohn wrote:
The state-space approach has the advantage in the appropriate situations
that you can model the trends and seasonals and cycles in a way that
doesn't assume stationarity and provides a lot of flexibility. To me a lot
of it depends on if the nature of th
The state-space approach has the advantage in the appropriate situations that
you can model the trends and seasonals and cycles in a way that doesn't assume
stationarity and provides a lot of flexibility. To me a lot of it depends on
if the nature of the irregularity is an inherent property of
On Thu, 3 Apr 2014, Roy Mendelssohn wrote:
How irregular is irregular. kalman filter based methods, such as those in
KFAS and DLM, can handle missing data, and often "irregular" data can be
thought of as regular data with missing values, A lot depends on how
irregular and how big the gaps, to th
HI Rich:
How irregular is irregular. kalman filter based methods, such as those in KFAS
and DLM, can handle missing data, and often "irregular" data can be thought of
as regular data with missing values, A lot depends on how irregular and how
big the gaps, to the point where the analysis can
On Thu, 3 Apr 2014, arun wrote:
Not sure if this helps you.
http://stackoverflow.com/questions/12623027/how-to-analyse-irregular-time-series-in-r
A.K.,
Yes, it does. I've read all the zoo docs I can find and have been
searching for more information on irregular time series data. Environment
I have irregular time series as zoo objects; one example:
structure(c(6, 5, 14, 9, 8, 9, 8, 5, 5, 5, 3, 3, 4, 3, 9, 6.94,
7.44, 3.09, 0.84, 5.35, 4.76, 4.21, 1.58, 2.6, 3.41, 9.59, 7.1,
5, 5, 5, 3, 1.5, 2.4, 3.9, 5.8, 2.3, 3.6, 4.1, 15.4, 7.8, 4.2,
5.8, 3, 4.5, 8.1, 9.6, 9.3, 7.9, 3.8, 3.2,
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