I'd also check out the pandas <http://pandas.pydata.org/> package, as it
might do what you want with timeseries. I'd also note that you should use
caution when doing Machine Learning on time-series, especially after you
smooth your data. This introduces autocorrelations into your signal which
might do bad things when using methods like cross-validation if you don't
calculate your splits correctly.
On Mar 10, 2015 11:34 PM, "Leo Kris Palao" <lk.pa...@gmail.com> wrote:
> Hi Gael,
>
> Thanks for your quick reply. My apologies.
>
> Thanks,
> -Leo
>
> On Wed, Mar 11, 2015 at 1:48 PM, Gael Varoquaux <
> gael.varoqu...@normalesup.org> wrote:
>
>> Hi,
>>
>> Filtering is off topic for scikit-learn as it is more related to signal
>> processing than machine learning.
>>
>> However, googling for "Savitzky-Golay filter Python" gives me:
>>
>> - http://docs.scipy.org/doc/scipy-dev/reference/signal.html: in the
>> latest scipy there is a Savitzky-Golay
>>
>> - http://wiki.scipy.org/Cookbook/SavitzkyGolay
>> Some code is out there to do Savitzky-Golay filtering
>>
>> Gaël
>>
>> On Wed, Mar 11, 2015 at 01:40:19PM +0800, Leo Kris Palao wrote:
>> > Hi Scikit Users,
>>
>> > I have a 11 years of MODIS NDVI data that I want to process for drought
>> > assessment. But first I want to smooth my NDVI time series data to
>> reduce/
>> > remove noise that will affect my assessment. Can you point me in the
>> right
>> > direction?
>>
>> > Thanks,
>> > -Leo
>>
>> >
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>>
>> --
>> Gael Varoquaux
>> Researcher, INRIA Parietal
>> Laboratoire de Neuro-Imagerie Assistee par Ordinateur
>> NeuroSpin/CEA Saclay , Bat 145, 91191 Gif-sur-Yvette France
>> Phone: ++ 33-1-69-08-79-68
>> http://gael-varoquaux.info
>> http://twitter.com/GaelVaroquaux
>>
>>
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_____________________________________
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Editor and Web Master | Berkeley Science Review
<http://sciencereview.berkeley.edu/>
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