Thu Nov 27 23:09:53 CST 2008
On Nov 27, 2008, at 11:40 AM, Pierre GM wrote:
> On Nov 27, 2008, at 11:23 AM, Robert Ferrell wrote:
>> That has a hole on Sep 1. This matters for things like moving
>> calculation. Sep 1 should be treated like a Saturday or Sunday, but
>> instead causes a 5-day mov_average calculation to not compute
>> from Sep 2 through Sep 7:
>> timeseries([-- -- -- -- 22.998 -- -- -- -- -- 21.06],
>> dates = [25-Aug-2008 ... 08-Sep-2008],
>> freq = B)
>> My question: What is a good way to handle (get rid of?) the holes in
>> the series?
> Mmh. On the top of my head, I'd do something like that:
> * create a new series by using .compressed on your initial series.
> You'll get rid of the masked data and will have incomplete dates, but
> it shouldn't matter.
> * use your moving average function on the new series.
> * if needed, reset the missing dates by using fill_missing_dates on
> the filtered series.
> Let me know how it goes.
Since the date arrays has holes, I can't use timeseries date range
calculations. So, for instance, to get the previous 5 days of data I
can't just use series[d-5:d]. Instead I need to (I think) convert to
an index, series.date_to_index(d), and then use that index. I'm
going to try that, along with using .compressed(), and see how I do.
Is there any possibility of allowing user defined frequencies?
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