Time-based rolling apply inconsistent behavior on NaN rows
#### Code Sample, a copy-pastable example if possible

```python
import pandas as pd
import numpy as np

s = pd.Series([1, 2, 3, np.nan, 5], index=pd.date_range('2017-01-01', '2017-01-05'))
print(s.rolling('3d', min_periods=1).apply(lambda x: 42))

```
#### Problem description

Output of the above code sample:

```
2017-01-01    42.0
2017-01-02    42.0
2017-01-03    42.0
2017-01-04     NaN
2017-01-05    42.0
```

It seems that the user function did not get applied to the window corresponding to the original NaN row, resulting in NaN as the result for that row. Why is that? The more reasonable output would be all 42 because by the `min_periods` constraint, all of the windows are valid.

Compare this to the equivalent fixed window version:

```python
s = pd.Series([1, 2, 3, np.nan, 5])
print(s.rolling(3, min_periods=1).apply(lambda x: 42))
```

which gives the following output:

```
0    42.0
1    42.0
2    42.0
3    42.0
4    42.0
```

Is this inconsistency between fixed and variable size window a desired behavior?

#### Expected Output
```
2017-01-01    42.0
2017-01-02    42.0
2017-01-03    42.0
2017-01-04    42.0
2017-01-05    42.0
```

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

<details>

INSTALLED VERSIONS
------------------
commit: None
python: 2.7.13.final.0
python-bits: 64
OS: Linux
OS-release: 4.11.9-1-ARCH
machine: x86_64
processor: 
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
LOCALE: None.None

pandas: 0.21.0.dev+316.gf2b0bdc9b
pytest: None
pip: 9.0.1
setuptools: 36.2.5
Cython: 0.26
numpy: 1.13.1
scipy: None
pyarrow: None
xarray: None
IPython: 5.4.1
sphinx: None
patsy: None
dateutil: 2.6.1
pytz: 2017.2
blosc: None
bottleneck: None
tables: None
numexpr: None
feather: None
matplotlib: None
openpyxl: None
xlrd: None
xlwt: None
xlsxwriter: None
lxml: None
bs4: None
html5lib: None
sqlalchemy: None
pymysql: None
psycopg2: None
jinja2: None
s3fs: None
pandas_gbq: None
pandas_datareader: None

</details>
