2013/9/26 Peter Prettenhofer <[email protected]>: > > > > 2013/9/26 Kyle Kastner <[email protected]> >> >> I had not thought about use inside a Pipeline - though now that you >> mention it, that seems like the ideal use case for an algorithm like this. >> Is this the PR you mentioned? >> https://github.com/scikit-learn/scikit-learn/pull/1454 >> >> As far as lagged features transformer - are we talking about rolling >> statistics? Something similar to pandas rolling_mean, rolling_apply, etc.? I >> have poorly reimplemented that using ```stride_tricks``` more times than I >> probably should have... > > > well... I was mostly thinking of fx val at lag_1, fx at lag_2, ... so > feature values at previous time steps.
Yes but also aggregate feature like windowing min, max, mean, median, diff, cumsuff... for various window size. -- Olivier http://twitter.com/ogrisel - http://github.com/ogrisel ------------------------------------------------------------------------------ October Webinars: Code for Performance Free Intel webinars can help you accelerate application performance. Explore tips for MPI, OpenMP, advanced profiling, and more. Get the most from the latest Intel processors and coprocessors. See abstracts and register > http://pubads.g.doubleclick.net/gampad/clk?id=60133471&iu=/4140/ostg.clktrk _______________________________________________ Scikit-learn-general mailing list [email protected] https://lists.sourceforge.net/lists/listinfo/scikit-learn-general
