On Mar 11, 1:27=A0pm, jameskuy...@[EMAIL PROTECTED]
wrote:
> I've got a time series 807793 bins long, with missing data in all but
> 48945 of those bins. Only 7392 of those bins have a non-zero event
> count. Those bins have a total count of about 1 million events, which
> tells you that events are highly clustered, at least at the time scale
> of the bin size (5 minutes).
>
> I want to use autocorrelation analysis to investigate the clustering
> of these events on longer time scales. The large amount of missing
> data makes such analysis difficult, but the non-missing data is
> clustered on time spans of 9 bins or so. Therefore, it seems to me
> that with the right algorithm, it should be possible to estimate the
> autocorrellation at lags of less than 9 bins. Does anyone know what
> the right algorithm would be?
Seems to me that this is an issue, I would use normal techniques on
subsets of the data. There might be other ways but clusters of
missing data are kinda like small data sets.
Cheers,
Brian
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Brian Larsen
Boston University
Center for Space Physics
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