[SciPy-User] scikits.timeseries for many, large, independent and irregular time series

Neil Hodgson hodgson.neil@yahoo.co...
Sun Oct 23 17:28:40 CDT 2011


I've been doing some work with something like this.

>> 1. I've seen posts discussing converting irregular timeseries to "proper" regularly spaced TimeSeries data.  

I have been keeping my eye on the excellent looking Pandas and scikits.timeseries (which plan to consolidate, see http://pandas.sourceforge.net/timeseries.html), but for the reason you describe above I've also so far stuck to some home-grown code.  It seems like lots of methods would need adapting to cope with non-uniformly sampled data (more common in geosciences compared to financial data for example).  I've been waiting for Numpy 1.7 and the new datetime64 dtype before investing any serious time and energy in to even thinking about it.  

>> 2. Some computations could involve very large TimeSeries objects. 

Here, I am using PyTables, with datetimes stored as float64.  I think it's perfect for what you describe.  (Pandas already is also using PyTables as an optional io platform).  

Hope that helps and I am interested to see what other people are doing,
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