[SciPy-User] Status of TimeSeries SciKit
Tue Aug 2 02:37:29 CDT 2011
> >> I agree. I already have 50% or more of the features in
> >> scikits.timeseries, so this gets back to my fragmentation argument
> >> (users being stuck with a confusing choice between multiple
> >> libraries). Let's make it happen!
> > So what needs to be done to move things forward?
> > Do we need to draw up a roadmap?
> > A table with functions that respond to common use cases in natual
> > science, computing, and economics?
> Having a place to collect concrete use cases (like your list from the
> prior e-mail, but with illustrative code snippets) would be good.
> You're welcome to start doing it here:
I will fill it with my stuff.
Shall we file feature request directly as issues?
> A good place to start, which I can do when I have some time, would be
> to start moving the scikits.timeseries code into pandas. There are
> several key components
> - Date and DateArray stuff, frequency implementations
> - masked array time series implementations (record array and not)
> - plotting
> - reporting, moving window functions, etc.
> We need to evaluate Date/DateArray as they relate to numpy.datetime64
> and see what can be done. I haven't looked closely but I'm not sure if
> all the convenient attribute access stuff (day, month, day_of_week,
> weekday, etc.) is available in NumPy yet. I suspect it would be
> reasonably straightforward to wrap DateArray so it can be an Index for
> a pandas object.
> I won't have much time for this until mid-August, but a couple days'
> hacking should get most of the pieces into place. I guess we can just
> keep around the masked array classes for legacy API support and for
> feature completeness.
I value very much the work of Pierre and Matt.
But my difficulti with the scikit was that the code is too complex. So I was
only able to contribute helper functions for doc fixes.
Please, lets make it happen that this effort is not a on or 3 man show but
results in something whcih can be maintained by the whole community.
Nevertheless, the timeseries scikit made my work more comfortable and
understadable than I was able to manage with R.
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