[SciPy-User] Status of TimeSeries SciKit

[Tim Michelsen]

>> Since most of my code for meteorological data evaluations is based on
>> it, I would be happy to receive infomation on the conclusion and how I
>> need to adjust my code to upkeep with new developments.
> 
> When it gets to that point I'd be happy to help (including looking at
> some of your existing code and data).
In short my process goes like:
* QC of incoming measurements data
* visualisation and statistics (basics, disribution analysis)
* reporting
* back & forcasting with other (modeled) data
* preparation of result data sets

When it comes to QC I would need:
* check on missing dates (i.e. failure of aquisitition equipment)
* check on double dates (= failure of data logger)
* data integrity and plausability tests with certain filters/flags

All these need to be reported on:
* data recovery
* invalid data by filter/flag type

So far, I have been using the masked arrays. Mainly because it is heaily
 used in the time series scikit and transfering masks from on array to
another is quite once you learned the basics.

Would you work these items out in pandas, as well?

P.S. Your presentation "Time series analysis in Python with statsmodels"
is really cool and has shown me good aspects about the HP filters

Regards,
Timmie