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 http://boost-sandbox.sourceforge.net/libs/time_series/doc/html/time_series/u... "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