Hugo Duncan wrote:
Hi,
And if anybody would like to peruse the documentation online, you can find it here:
http://boost-sandbox.sourceforge.net/libs/time_series/doc/html/
Since I am currently doinng some time series work, I thought I would have a look. First glance shows an impressive amount of clear thinking and extensability.
Thanks.
Below are some use cases that are important to me, and which I can not immeadiatley see as possible within the framework.
I see no mention of how to handle large time series that need processing incrementally. There are a lot of concepts in there, so I may well have missed something. At the moment I handle these by reading the data into a circular buffer with enough capacity to provide as much time history as I need for the algorithms to run at each time point. Using some imagination, I can see using the library to do this in either of two ways.
The first would be to use the existing series types and update the series values for each new datapoint - but I don't see a way of discarding data points.
The second would be to define a new series type that accepted new values, discarded old values, and generally did all the book keeping - is that possible within the confines of the framework?
I don't think either of these is the right approach. It shouldn't be the series' job to keep a circular buffer of data for the algorithm to use. Rather, if the algorithms requires a buffer of previously seen data, it should cache the data itself, as in the rolling average implementation I sent around a few days ago. The Sequence concept on which all the time series' are built requires readable and incrementable cursors. That means the time series algorithms *should* all work with an "input" or single-pass series types -- that is, one with a destructive read. That would be the way to go IMO. I could see a time series type implemented in terms of std::istream that reads runs from std::cin, for instance. Or more practially, one that memory-maps parts of a huge file and traverses it with single pass cursors. This would be a very interesting time series! The algorithms haven't been tested with such a single pass series, but I don't see a fundamental problem with it.
I also have data that has non-constant sampling periods. At the moment I handle these by piecewise sampling to another (constant period) timebase. Can this fitted into the framework?
I'm not 100% sure I understand your use case. But most of the series types and algorithms allow non-discrete sequences. That is, the offsets can be floating point. Could that help?
Finally, I don't see a convolution algorithm for applying filters. Probably easy enough to implement, and is in my view important enough to include in the core algorithms.
Yup, no convolution yet. Sure would be nice. Patches welcome! :-) -- Eric Niebler Boost Consulting www.boost-consulting.com The Astoria Seminar ==> http://www.astoriaseminar.com