Predict fails with "cannot allocate vector of size..." with large number of prediction points

Hi,
I am currently trying to use Prophet to predict byte flows in a network. The data set has the following properties:

* 4 periods (1 period = 1 week)
* minute granularity
* 44355 data points

The first couple of lines look as follows, with y being the number of bytes monitored in the network:

\# | ds | y
------------ | ------------- | -------------
1 | 2016-08-18 19:00:00 | 8476391449
2 | 2016-08-18 19:01:00 | 6432109555
3 | 2016-08-18 19:02:00 | 4039378069
4 | 2016-08-18 19:03:00 | 4124949796
5 | 2016-08-18 19:04:00 | 6911448995
6 | 2016-08-18 19:05:00 | 4021931960

I wrote some simple R code to fit the model and predict a single period:

```
fit <- prophet(data)
future <- make_future_dataframe(fit, periods = 1)
forecast <- predict(fit, future)
plot(fit, forecast)
```

The model fitting works, however, during the prediction, R tries to allocate more and more memory until both Mem and Swp are fully allocated (I have a machine with 32GB of RAM, Debian 8.8 Jessie, R 3.3.3). Meanwhile, a single CPU core is at 100% load. The program will eventually crash with the following error:

```
Error: cannot allocate vector of size 242.4 Mb
Execution halted
Warning message:
system call failed: Cannot allocate memory
```

I've done the same thing with the Holt-Winters prediction that's built into R already which works flawlessly and uses around 300MB of RAM.

Any ideas what the problem is here? Could it be the granularity?