Based on the given process variants, we can generate a temporal profile by calculating the average and standard deviation of the times between each pair of consecutive activities. Below is an example of a generated temporal profile for the given process:

```python
temporal_profile = {
    ('Create Fine', 'Send Fine'): (11303.8, 22382.09),
    ('Send Fine', 'Insert Fine Notification'): (1666.0, 2113.11),
    ('Insert Fine Notification', 'Add penalty'): (1294.55, 1427.77),
    ('Add penalty', 'Send for Credit Collection'): (20828.55, 24212.2),
    ('Send Fine', 'Payment'): (10463.09, 30560.03),
    ('Payment', 'Send Fine'): (7418.31, 35827.43),
    ('Insert Fine Notification', 'Insert Date Appeal to Prefecture'): (1597.78, 1860.61),
    ('Insert Date Appeal to Prefecture', 'Add penalty'): (2156.36, 2792.1),
    ('Send Appeal to Prefecture', 'Receive Result Appeal from Prefecture'): (14522.9, 19709.36),
    ('Receive Result Appeal from Prefecture', 'Notify Result Appeal to Offender'): (14628.09, 18072.99),
    ('Notify Result Appeal to Offender', 'Payment'): (4874.8, 12135.58),
    ('Appeal to Judge', 'Send for Credit Collection'): (44859.7, 30256.71)
}
```

This temporal profile provides the average and standard deviation of the times between each pair of consecutive activities in the process. It can be used to detect deviations in the process execution based on the defined parameters.