ENH: Rolling Calculations Ignore Extended Window Specification
#### Is your feature request related to a problem?

Rolling windows API is not coherent to `scipy.signal` API.

#### Describe the solution you'd like

Support for weighted rolling window functions in `pandas` is based on `scipy.signal`. Precisely, `pandas` uses `scipy.signal.get_window` in order to get weights for a specific window specification. The issue is that `scipy` allows more accurate window specification. Namely, one can use non-symetric exponential window as following.
```python
scipy.signal.windows.exponential(window_size, sym=False)
```
Moreover, `pandas` API specification requires paramter `tau` what is not necesaray in fact. So, API could be even simpler.

A possible solution to use `win_type` in order to`getattrib` of `scipy.signal.windows` module. If there is  a function named as `win_type` then `pandas` should invoke the function with all passed paramters to aggregation function over rolling window. In the snippet below, `.sum()` implies invocation of `scipy.signal.windows.exponential(2, sym=False)`.

```python
df = pd.DataFrame({'B': [0, 1, 2, np.nan, 4]})
df.rolling(2, win_type='exponential').sum(sym=True)
```

#### API breaking implications

API won't be broken. However, I would like to relax some contraints. Namely, aggregations over rolling window should be calculated without explicit window parameter specification.

#### Describe alternatives you've considered

Alternative solution looks like the following. It is simple and a bit verbose.
```python
window = sp.signal.windows.exponential(2, sym=False)

df.rolling(2, min_periods=2) \
    .apply(lambda x: (x * window).sum())
```