Zach Laine wrote:
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.
You can. Only the clip() view provides STL-style iterators that make the storage appear sparse. But if you look deeper, you can see that all the TimeSeries types export cursors (sequence::begin(series) and sequence::end(series)) that traverse only the non-zeros. For instance, given a TimeSeries, range_run_storage::for_each() will call a user-specified predicate, passing the value, offset and end-offset of every non-zero run in the series. Most of the time series algorithms are implemented in terms of this lower-level interface to deal efficiently with sparse data.
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.
Not a problem. You can have a series with a floating point discretization and an integral offset type. Consider the following program: #include <iostream> #include <boost/time_series/sparse_series.hpp> #include <boost/time_series/ordered_inserter.hpp> #include <boost/time_series/numeric/numeric.hpp> using namespace boost; using namespace time_series; int main() { sparse_series< double, double > s1(discretization = 3.7367); make_ordered_inserter(s1) (1.1, 1) (1.1, 2) .commit(); sparse_series< double, double > s2(discretization = 3.7367); make_ordered_inserter(s2) (1.1, 2) (1.1, 3) .commit(); sparse_series< double, double > s3 = s1 + s2; std::cout << s3 << std::endl; return 0; } The series type "sparse_series<double, double>" has a value type of double, a discretization of double, and an offset type which defaults to int. The discretization of both series is initialized to 3.7367. When you add the series, the discretizations are compared at runtime (it would assert if they were not the same), and the resulting series has the same discretization value. The integral offsets into such a series represent integral multiples of the floating point discretization.
Typo: In http://boost-sandbox.sourceforge.net/libs/time_series/doc/html/time_series/u... "container" is misspelled "contaier".
Well spotted.
These issues aside, I really like the formalisms introduced by the library, and think it will be very useful.
Thanks for your feedback. -- Eric Niebler Boost Consulting www.boost-consulting.com