Cross Validation Without Weekend Data Along with Meaning of Cross Validation?

@bletham @tcuongd

I am running the prophet model without weekend data in the initial dataset. When creating a future dataframe, I remove weekends.

```
new<- df %>%
group_by(group) %>%
mutate(weekdays = weekdays(ds)) %>%
do(predict(prophet(., daily.seasonality = TRUE, yearly.seasonality = TRUE),
filter(make_future_dataframe(prophet(., daily.seasonality = TRUE, yearly.seasonality = TRUE), periods = 14), weekdays(ds) != "Saturday" & weekdays(ds) != "Sunday"))) %>%
select(ds, group, yhat)
```

When doing `cross_validation` function with cutoffs and creating multiple models, will the diagnostic values (RMSE, MSE, etc.) be skewed because we cannot remove weekend data in the future dataframe?

```
diagnostics_2 <- df %>%
group_by(group) %>%
do(cross_validation(prophet(., daily.seasonality = TRUE, yearly.seasonality = TRUE),
initial = 365.25, period = 180, horizon = 14, units = 'days')
%>% performance_metrics()
)
```

My assumption would be yes and I would need to do the cross validation manually (not using the cross_validation function) with cutoffs and creating a future dataframe with weekends removed. Then calculate the diagnostic metrics manually.

(I'm doing this using R by the way)

My second question is that the result currently is by horizon. If I just want one number, per group (specifically RMSE), would you recommend to average all the RMSE values?

```
# A tibble: 26 x 9
# Groups: group [2]
group horizon mse rmse mae mape mdape smape coverage

1 AAMC 2 days 2.03 1.42 1.40 0.0669 0.0762 0.0694 1
2 AAMC 3 days 4.87 2.21 2.03 0.108 0.0767 0.104 0.667
3 AAMC 4 days 5.03 2.24 2.03 0.112 0.0762 0.107 0.667
4 AAMC 5 days 5.38 2.32 2.13 0.117 0.0866 0.112 0.667
5 AAMC 6 days 5.82 2.41 2.19 0.118 0.120 0.111 0.333
6 AAMC 7 days 8.26 2.87 2.59 0.147 0.190 0.135 0.333
7 AAMC 8 days 5.73 2.39 1.96 0.112 0.175 0.103 0.444
8 AAMC 9 days 2.33 1.53 1.28 0.0681 0.0388 0.0663 0.833
9 AAMC 10 days 5.21 2.28 1.87 0.101 0.0530 0.0943 0.667
10 AAMC 11 days 14.1 3.76 3.21 0.195 0.212 0.172 0.333
11 AAMC 12 days 18.1 4.25 3.64 0.230 0.213 0.198 0.333
12 AAMC 13 days 20.4 4.52 4.05 0.249 0.213 0.213 0.333
13 AAMC 14 days 22.3 4.72 4.38 0.267 0.207 0.228 0
14 AAU 2 days 0.0634 0.252 0.229 0.481 0.264 0.374 0
15 AAU 3 days 0.0388 0.197 0.182 0.456 0.264 0.510 0
16 AAU 4 days 0.0190 0.138 0.124 0.336 0.251 0.431 0
17 AAU 5 days 0.0192 0.139 0.127 0.347 0.265 0.444 0
18 AAU 6 days 0.0572 0.239 0.202 0.499 0.685 0.552 0
19 AAU 7 days 0.0559 0.236 0.206 0.500 0.681 0.561 0
20 AAU 8 days 0.0597 0.244 0.201 0.420 0.669 0.383 0
21 AAU 9 days 0.0717 0.268 0.238 0.466 0.669 0.363 0
22 AAU 10 days 0.0777 0.279 0.266 0.633 0.747 0.701 0
23 AAU 11 days 0.0300 0.173 0.156 0.424 0.399 0.562 0
24 AAU 12 days 0.0312 0.177 0.163 0.437 0.385 0.583 0
25 AAU 13 days 0.0720 0.268 0.240 0.586 0.768 0.680 0
26 AAU 14 days 0.0538 0.232 0.216 0.517 0.537 0.640 0
```

Result would therefore be:
```
RMSE
AAMC: 2.84
AAU: 0.21
```