On Sat, Dec 12, 2009 at 8:08 PM, THOMAS BROWNE <totalb...@mac.com> wrote: > Hello all, > > Quite new to numpy / timeseries module, please forgive the elementary > question. > > I wish to do quite to do a bunch of multivariate analysis on 1000 different > financial markets series, each holding about 1800 data points (5 years of > daily data). > > What's the best way to put this into a TimeSeries object? Should I use a > structured data type (in which case I can reference each series by name), or > should I put it into one big numpy array object (in which case I guess I'll > have to keep track of the series name in an internal structure)? What are the > advantages and disadvantages of each? > > Ideally I'd have liked the ease and simplicity of being able to reference > each series by name, while maintaining the fast speed and clean structure of > one big numpy array. Any way of getting both? > > Once I have a multivariate TimeSeries, how do I add another series to it?
I'm not sure if your TimeSeries object refers to the scikits or writing your own application. In the later case, I would recommend looking at the following for data handling, the first two are written or co-written with finance applications in mind, the last is a nice package for working with structured arrays, but I'm don't know about how extensive time handling is. http://code.google.com/p/pandas/ general (2d) panel data, arbitrary axis labels possible, integrates scikits.statsmodels http://scikits.appspot.com/timeseries based on masked arrays, extensive time handling http://pypi.python.org/pypi/tabular If you need to do more serious data work and don't have a special requirement on the data structure, I think, it would be better to use ( and contribute for adjustments/extensions/testing) to one of the existing data structures than writing your own. All 3 are BSD or MIT licensed. Josef > > Thanks for the help. > > Thomas. > > > _______________________________________________ > NumPy-Discussion mailing list > NumPy-Discussion@scipy.org > http://mail.scipy.org/mailman/listinfo/numpy-discussion > _______________________________________________ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
