html_url,issue_url,id,node_id,user,created_at,updated_at,author_association,body,reactions,performed_via_github_app,issue
https://github.com/pydata/xarray/issues/1978#issuecomment-371989690,https://api.github.com/repos/pydata/xarray/issues/1978,371989690,MDEyOklzc3VlQ29tbWVudDM3MTk4OTY5MA==,6815844,2018-03-10T01:18:11Z,2018-03-10T01:18:11Z,MEMBER,"Yes. 
```python
In [7]: da.rolling(date=3).construct('rolling_date')
Out[7]: 
<xarray.DataArray (item: 2, date: 6, rolling_date: 3)>
array([[[nan, nan,  0.],
        [nan,  0.,  1.],
        [ 0.,  1.,  2.],
        [ 1.,  2.,  3.],
        [ 2.,  3.,  4.],
        [ 3.,  4.,  5.]],

       [[nan, nan,  6.],
        [nan,  6.,  7.],
        [ 6.,  7.,  8.],
        [ 7.,  8.,  9.],
        [ 8.,  9., 10.],
        [ 9., 10., 11.]]])
Dimensions without coordinates: item, date, rolling_date
```
does the similar thing (`rolling_window` in your example).

FYI, using `sum` without skipna option for such a *strided* DataArray (in your `test_rolling_window_values`) is not a good idea.
We internally use `np.nansum` and this copies the entire array once. It throws away the advantage of the strided trick.
`sum(skipna=False)` is memory efficient.
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