[ANN] Boost.Time_series: feedback requested
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. -- Eric Niebler Boost Consulting www.boost-consulting.com
Hello
Maybe you could add a few lines to the docs on how to install the library.
My apologies if the section already exists. I dowloaded the zip and I am not
sure what exactly I should do. Maybe it is obvious to Boost developpers but
for outsiders of my kind that could be useful.
Thanks
"Eric Niebler"
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.
-- Eric Niebler Boost Consulting www.boost-consulting.com
Hi John, (continued below) John Christopher wrote:
Hello Maybe you could add a few lines to the docs on how to install the library. My apologies if the section already exists. I dowloaded the zip and I am not sure what exactly I should do. Maybe it is obvious to Boost developpers but for outsiders of my kind that could be useful. Thanks
<snip>
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.
Good suggestion. I'll add an installation section to the docs. The library is header-only, so there is no lib to compile and link to. Boost.Time_series requires either Boost 1.34 or Boost CVS HEAD. If you already have a Boost installation with bjam, then just unpack time_series.zip into some empty directory, set a BOOST_ROOT environment variable to point at the root of your boost installation, and then you should be able to compile the example program (libs/time_series/example) with "bjam --v2" If you're a Visual C++ user, the example program has a .vcproj file, which will also get you going, so long as you set the BOOST_ROOT environment variable. If you're winging it, you just need to set the include directories like this: -I /home/eric/time_series/boost/backports \ -I /home/eric/time_series \ -I $BOOST_ROOT The backports include path must come before $BOOST_ROOT, and is only necessary if you are compiling against Boost 1.34. (It's not needed if you're compiling against HEAD.) HTH, -- Eric Niebler Boost Consulting www.boost-consulting.com
participants (2)
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Eric Niebler -
John Christopher