To generate a temporal profile for the given process, we need to calculate the average and standard deviation of the times between each pair of activities that occur in at least one process execution.

Here is a Python code snippet that calculates the temporal profile for the given process variants:
```python
from collections import defaultdict
from statistics import mean, stdev

# Define a dictionary to store the temporal profile
temporal_profile = defaultdict(lambda: (0, 0))

# Process variants
variants = [
    ['Create Fine', 'Send Fine', 'Insert Fine Notification', 'Add penalty', 'Send for Credit Collection'],
    ['Create Fine', 'Payment'],
    # ... include the rest of the process variants here
]

# List of tuples representing the times between each pair of activities
times_list = []

# Iterate over each process variant
for variant in variants:
    # Iterate over each pair of activities in the variant
    for i in range(len(variant) - 1):
        activity1 = variant[i]
        activity2 = variant[i + 1]
        # Calculate the time between the two activities
        time = 1  # replace this with the actual time between the two activities
        # Add the time to the list of times
        times_list.append(time)
        # Update the temporal profile for the pair of activities
        n = len(times_list)
        avg = mean(times_list)
        stddev = stdev(times_list)
        temporal_profile[(activity1, activity2)] = (avg, stddev)

# Print the temporal profile
for pair, stats in temporal_profile.items():
    print(f"{pair}: ({stats[0]}, {stats[1]})")
```
Note that the code snippet above assumes that the time between each pair of activities is 1. In practice, you would need to replace this with the actual times between the activities.

Here is an example of what the output might look like:
```css
('Create Fine', 'Send Fine'): (1.0, 0.0)
('Send Fine', 'Insert Fine Notification'): (1.0, 0.0)
('Insert Fine Notification', 'Add penalty'): (1.0, 0.0)
('Add penalty', 'Send for Credit Collection'): (1.0, 0.0)
('Create Fine', 'Payment'): (1.0, 0.0)
# ...
```
This shows the average and standard deviation of the times between each pair of activities in the process.