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From: Zach Laine (whatwasthataddress_at_[hidden])
Date: 2006-12-14 14:42:05

I use time series in the signal processing domain. I think there are
a couple of valid use cases there that may not apply in the financial
domain, and so got left out.

The Hello, World! example says: "iterators ... traverse both the zeros
and the non-zeros, even if the storage is sparse". What if I want to
iterate over only the actual sparse elements? This is actually a very
common use case for me. I'd like to be able to do both full and
sparse iteration.

Commonly in signal processing, you have uniformly spaced sample times,
but they are a multiple of some non-integral time increment. This
seems to be impossible to specify using Boost.Time_series as-is. From
my understanding of the docs, it seems that you cannot have
uniformly-spaced floating-point offsets without resorting to
dense_series<std::pair<TimeType, ValueType> >. Specifically, this
note: "Some of the numeric algorithms do not work with series that
have floating-point offsets. For instance, partial_sum() assumes
integral offsets; in fact, the discrete nature of the algorithm
prohibits its use with any series with floating-point offsets."
bothers me. What if I have a time interval that is 3.7367ms, but it
is completely regular? My alternatives appear to be a time series and
an extrinsic timestep value, which I must multiply by the time series
index to get back the actual timestamp of a series element, or the
pair I mentioned above.

Typo: In
"container" is misspelled "contaier".

These issues aside, I really like the formalisms introduced by the
library, and think it will be very useful.

Zach Laine

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