Eric Niebler wrote:
I'm pleased to announce the availability of a new library for computing with time series (http://en.wikipedia.org/wiki/Time_series). From the documentation:
The purpose of the Boost.Time_series library is to provide data structures, numerical operators and algorithms to operate on time series. A time series is a series of data points, sampled at regular intervals. The library provides numerous time series containers, each with different time/space trade-offs, and a hierarchy of concepts which allow the time series to be manipulated generically. The library also provides operators and algorithms which use the generic interfaces to perform calculations on time series and accumulate various statistics about them.
Boost.Time_series does not yet contain all the algorithms one might want in order to perform full time series analysis. However, the key contribution of Boost.Time_series is the framework and the rich hierarchy of concepts with which such algorithms can be written to efficiently and generically process series of data with widely divergent in-memory representations and performance characteristics. Boost.Time_series provides several such series containers, as well as mechanisms for defining additional series types and algorithms that fit within the framework. Some examples of series types that are provided are: dense, sparse, piecewise constant, heaviside and others, as well as adaptors for providing shifted, scaled and clipped series views.
The documentation can be found here: http://boost-sandbox.sf.net/libs/time_series/doc/html/index.html
The code is in time_series.zip, which can be downloaded here: http://boost-consulting.com/vault/index.php?directory=Math%20-%20Numerics
Any suggestions for improvements are most welcome.
Hi Eric - Looks very interesting. Feedback here is based on extremely cursory look at the docs. And I'll say that I'm not a huge domain expert on the mathematics, but I've run into some pretty simple time series in couple of apps over the years. In particular, I'm sorta surprised there isn't a moving average algorithm as that's time series algorithm with a really wide applicability. Classic example -- calculate a 10 day rolling average of the closing price of some stock. Or calculate a 20 minute rolling average of the trading prices or quotes. And, so on...there are all sorts of other applications for rolling averages. So now for the *other* question. Am I correct in seeing that with the time series facade that I might be able to plug in a different offset_type? Of course, what I'm thinking is for the stock price rolling average example above you might want to use gregorian::date or posix_time::ptime to represent point of measurement of the stock price. I wasn't finding the requirements for the offset_type...which is what I think would need to be replaced. I also find the Discretization intervals interesting. Is that a standard way of handling the breakdown for statistical purposes? The thing being that only days and weeks are actually fixed lengths in the *messy world*...months, quarters, etc are all different depending leap years and such. Anyway, nice job as usual :-) Jeff