Unable to set origin of inter-day aggregated intervals
Having a series like the one bellow, which I resample to `'2D'` frequency and the sum the resulting resampler:

```python
>>> dates = pandas.date_range('2019-01-01', '2019-01-10', freq='D')[[0, 4, 5, 8]]
>>> dates
DatetimeIndex(['2019-01-01', '2019-01-05', '2019-01-06', '2019-01-09'], dtype='datetime64[ns]', freq=None)
>>> pandas.Series(index=dates, data=[0, 1, 2, 3]).resample('2D').sum()
2019-01-01    0
2019-01-03    0
2019-01-05    3
2019-01-07    0
2019-01-09    3
Freq: 2D, dtype: int64
```

As a result, I got an index starting on the first date of the provided dates.

However, if I slightly change the dates so that the first one is `'2019-01-02'` instead of `'2019-01-01'`, I get completely different results:

```python
>>> dates2 = pandas.date_range('2019-01-01', '2019-01-10', freq='D')[[1, 4, 5, 8]]
>>> dates2
DatetimeIndex(['2019-01-02', '2019-01-05', '2019-01-06', '2019-01-09'], dtype='datetime64[ns]', freq=None)
>>> pandas.Series(index=dates2, data=[0, 1, 2, 3]).resample('2D').sum()
2019-01-02    0
2019-01-04    1
2019-01-06    2
2019-01-08    3
Freq: 2D, dtype: int64
```

There is no way to specify a base/origin date for the aggregated intervals like there is for intra-day resampling, right?

Use case
------------

Having multiple time series, not necessarily periodic, not necessarily starting/ending in the same dates, I would like to calculate the correlation between them. To do so, I first need to aggregate the results with a period between 1 day and 1 week (`'2D'`, `'3D'`, `'4D'`...). In order to be able to match the correlation I need indexes to be aligned with respect to a common start date.

Same problem would show up if I wanted to aggregate multiple weeks, months, years... (`'3W'`, `'5M'`, `'2Y'`...)