Would the output be different if you simply wrapped the whole process with reshaping 3D input to 2d?
On Wed, Dec 5, 2018 at 7:14 PM lampahome <pahome.c...@mirlab.org> wrote: > I want to regress time series prediction per week, so the unit of train > data X is the day ex: Mon, Tue, Wed...etc. > > Ex: train data X is like below > X: > [ [1,2,3,4,3,2,1] > ,[2,2,3,4,3,2,2] ] > Each data of each row is about the day of one week. So each row has 7 data. > > Now if I have another feature W in each day like weather, or traffic or > else. > > I thought expand the X to 3d is reasonable because the W should be > contained in each day in X. > > So what I thought X is: > [ [ [1, W-Mon], [2, W-Tue] , [3, W-Wed] , [4, W-Thu] , [3, W-Fri] , > [2, W-Sat] , [1, W-Sun] ] > , [ [2, W-Mon], [2, W-Tue] , [3, W-Wed] , [4, W-Thu] , [3, W-Fri] , > [2, W-Sat] , [2, W-Sun] ] ] > It become a 3d input and contain every feature of each day. > > Does scikit have regression algo can accept the 3d input X ? > I almost found algo can only accept 2d input X ex: *X* : array-like or > sparse matrix, shape = [n_samples, n_features] > > > _______________________________________________ > scikit-learn mailing list > scikit-learn@python.org > https://mail.python.org/mailman/listinfo/scikit-learn >
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