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
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 http://boost-sandbox.sourceforge.net/libs/time_series/doc/html/time_series/user_s_guide/extensibility.html
"container" is misspelled "contaier".
These issues aside, I really like the formalisms introduced by the
library, and think it will be very useful.
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