[SciPy-dev] time series implementation approach

Pierre GM pgmdevlist at gmail.com
Wed Dec 13 05:06:17 CST 2006


David,
Merci beaucoup pour les sources.
I guess a lot of people in this list have their own implementation of 
timeseries...

Storing a date along data gives indeed the possibility to deal with gapped 
series. However, things becomes quite messy when you have to combine several 
series: how do you deal with the gaps? Your approach (for the glance I got) 
assumes either a step or a linear interpolation, which should work nicely in 
your case, but is doubtfully applicable to other cases. 

In fact, you just gave me an idea. In maskedarrays, the mask can be nomask 
(viz, no masked data, surprise), which greatly simplifies most operations. 
Here, we could have a frequency of nofreq, which would indicate that the time 
step is not constant: one simple option is then to cast it to the smallest 
reasonable timestep, and set the missing  values to "masked". (By reasonable, 
I mean average: if most of your data is roughly monthly but for a couple of 
daily ones, stick to monthly. Unless you really want to go daily. Dammit, we 
gonna have to leave this possibility).

So yeah, we may eventually have to consider varying frequencies. Which would 
mean that the shifting array approach will have to be reexamined. Maybe not: 
if freq is nofreq, then it'll panic, for sure. But if freq is set, then it 
remains a quite viable option.

OK, now I stop thinking aloud. David, could you tell us more about what you 
need ? What kind of data do you have to work with ? Are missing dates 
something you have to deal with on a very regular basis ?


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