rolling method: use time window and win_type
I have the same problem with this [StackOverflow post](https://stackoverflow.com/questions/47377581/pandas-rolling-specifying-time-window-and-win-type), I reproduce it here. The goal is to calculate weighted time-based moving average over an irregular time series

#### Code Sample

```python
import pandas as pd
from pandas.tseries.frequencies import to_offset

dft = pd.DataFrame(
    {'B': [0, 1, 2, 3, 4]},
    index = pd.Index(
        [
            pd.Timestamp('20130101 09:00:00'),
            pd.Timestamp('20130101 09:00:02'),
            pd.Timestamp('20130101 09:00:03'),
            pd.Timestamp('20130101 09:00:05'),
            pd.Timestamp('20130101 09:00:06')
        ],
        name = 'foo'
    )
)
dft.rolling('2s').sum() # worked
dft.rolling('2s', win_types='triang').sum() # error: Invalid window
dft.rolling(to_offset('2s'), win_types='triang').sum() # error: Invalid window
dft.rolling(pd.Timedelta('2s'), win_types='triang').sum() # error: Invalid window
```
#### Problem description

the `.rolling` method doesn't accept a time window and not-default window type

I look at the [documentation](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.rolling.html) and try with offset window but still have the same problem

If it's not possible to use time window, could you please update the documentation

#### Output of ``pd.show_versions()``

<details>

```
commit: None
python: 3.6.8.final.0
python-bits: 64
OS: Windows
OS-release: 10
machine: AMD64
processor: Intel64 Family 6 Model 78 Stepping 3, GenuineIntel
byteorder: little
LC_ALL: None
LANG: en
LOCALE: None.None

pandas: 0.24.2
pytest: None
pip: 19.1.1
setuptools: 41.0.1
Cython: None
numpy: 1.16.4
scipy: 1.2.1
pyarrow: None
xarray: None
IPython: 7.5.0
sphinx: 2.1.0
patsy: 0.5.1
dateutil: 2.8.0
pytz: 2019.1
blosc: None
bottleneck: None
tables: None
numexpr: None
feather: None
matplotlib: 3.1.0
openpyxl: 2.6.2
xlrd: 1.2.0
xlwt: None
xlsxwriter: None
lxml.etree: None
bs4: None
html5lib: None
sqlalchemy: 1.3.4
pymysql: None
psycopg2: None
jinja2: 2.10.1
s3fs: None
fastparquet: None
pandas_gbq: None
pandas_datareader: None
gcsfs: None
```

</details>
