[R] Time Series Decomposition On Zoo Objects: Errors
roy.mendelssohn at noaa.gov
Fri Apr 4 00:04:15 CEST 2014
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 the data themselves or of the observation process - for example if it makes sense to say the physical observable is there every month but we just have not been able to observe. I have fit state-space models to reasonably sparse data with what appear to be good results.
If you want I can send you some examples off-line.
On Apr 3, 2014, at 2:54 PM, Rich Shepard <rshepard at appl-ecosys.com> wrote:
> 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 the point where the analysis can be
>> calculated but is not very meaningful.
> The degree of irregularity varies with the data set. It varies greatly.
> Sometimes, monthly samples are missed because the streambed is dry, or
> because there are several meters of snow on top of the channel. Sometimes
> regulators have permit holders stop analyzing for a chemical constituent,
> then there's an agency staff change and that constituent is back on the list
> of chemicals to be monitored. Some times fish are present, other times
> they're not.
>> Don't know if this helps.
> Yes, it does. I'll read up on Kalman filters, on state-space models with
> missing data modifications in Shumway & Stoffer (3rd Ed.), and on gamm.
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Supervisory Operations Research Analyst
Environmental Research Division
Southwest Fisheries Science Center
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e-mail: Roy.Mendelssohn at noaa.gov (Note new e-mail address)
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