Hi Srinath, I will try this with some large datasets and will find and read few articles related to "moving window" approach.
Yes, this was written in IPython notebook using Scikit-learn and Matplotlib. Thanks, Upul On Mon, Nov 30, 2015 at 8:28 AM, Srinath Perera <[email protected]> wrote: > I am adding dev@ for record. > > Upul, this is beautiful :) !! > > I think we can do this with released components? Basically build the model > in ML and use that in IoT product then. > > Also I think we should try this with more datasets + read about this ( I > guess someone must have done this before), and add it to ML wizard. > Currently we do not have a good way to predict autocorrelated data. > > Nirmal, we should use the same with United secuirty check time use case. > > I will be in Trace in the PM. Let's chat. > > Thanks > Srinath > > p.s. what are you using to try this out? (ipynb thing). > > On Sun, Nov 29, 2015 at 10:17 PM, Upul Bandara <[email protected]> wrote: > >> Hi Srinath, >> >> As we discussed Friday morning, few time series models were trained using >> simple linear regression with moving windows. >> Three small (but real-world) datasets were used for training those >> models. >> >> According to the generated prediction graphs, it looks like "moving >> windows" are capable of (correctly) identifying trends in time series >> graphs. >> >> Prediction graphs are shown in [1]. >> >> >> [1]. >> https://github.com/upul/scratch/blob/master/Time_Series_Using_Linear_Regression.ipynb >> >> Thanks, >> Upul >> >> -- >> Upul Bandara, >> Mob: +94 715 468 345. >> > > > > -- > ============================ > Blog: http://srinathsview.blogspot.com twitter:@srinath_perera > Site: http://people.apache.org/~hemapani/ > Photos: http://www.flickr.com/photos/hemapani/ > Phone: 0772360902 > -- Upul Bandara, Mob: +94 715 468 345.
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