I have several years of univariate wind speed data to which I would like to
apply singular spectrum analysis. The data are sampled every 15min and a year
is a fundamental periodicity, which suggests L=35,040 values.
I would like to fill the gaps. The missing values are scattered at low density
throughout the series. I doubt there is a block of even one month that doesn't
have at least a couple pieces of missing data, but I'd surprised to learn the
total number are prohibitive.
The filling routines in Rssa like igapfill assume a shaped ssa object, so it
seems I need to run ssa successfully first before I can fill. When I try this
with L=35,040 or anything above about 2,000 I get an error message
Nothing to decompose: the given field shape is empty
and warnings like
Some field elements were not covered by shaped window. 42646 elements will be
ommited.
This is frustrating, because if I manually fill missing data with the series
mean, which I understand as being the first step of igapfill, the decomposition
succeeds with L=35040. The operation seems efficient and the spectral
components look as expected. But since I have manually created a series with no
missing data, this doesn't help me with gap filling.
To concoct a shaped ssa object with the original missing pattern, I invoked
force.decompose=FALSE. At that point I can bring my task to completion, but I
don't know what I'm doing. The only examples I see in the docs are not
explained and are in 2D.
Can someone familiar with this kind of use case explain what the purpose of
force.decompose and explain the best practice given my missing data situation?
Are there consequences to my workaround? Thanks.
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