Sanjeev Mohindra wrote:
Just doing through it now. It would be very useful. Some quick questions and comments based on very brief perusal.
1. How does one handle missing (time-series) values in this framework? So, for example, I have set up my time-series with a certain discretization and then at some point(s) along the series, I receive no data, what would be an elegant way of dealing with this situation in the library. We would also need a policy to ignore or interpolate the values of missing data.
There are a couple of options. One would be to use a sparse time series, and only insert (value,offset) elements for the data points you receive. Another option would be to pick a "magic" number and use that in the place of missing data points. If the data is dense otherwise, this would be the most space-efficient way to store the data. Either way, you'll need a pass over the data to interpolate the missing data.
2. I like the resampling support (Coarse_grain and Fine_grain)
Interpolation is one resampling strategy that I would very much like to add. It's a common use, and the library should support it.
3. Did I say, the library would be extremely useful.
Thanks! -- Eric Niebler Boost Consulting www.boost-consulting.com The Astoria Seminar ==> http://www.astoriaseminar.com