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]
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