Sure, I can help you generate a temporal profile for the given process. Here's a Python script that calculates the temporal profile based on the process variants you provided:

```python
import statistics

# Process variants data
process_variants = [
    ('Create Fine', 'Send Fine', 8380516.026, 20385),
    ('Create Fine', 'Payment', 889688.400, 46371),
    ('Send Fine', 'Insert Fine Notification', 3600.0, 20385),
    ('Insert Fine Notification', 'Add penalty', 3600.0, 20385),
    # ... (add the rest of the process variants here)
]

# Calculate the temporal profile
temporal_profile = {}
for activity1, activity2, time, frequency in process_variants:
    key = (activity1, activity2)
    if key not in temporal_profile:
        temporal_profile[key] = {"time_sum": 0.0, "time_count": 0, "time_squares": 0.0}
    temporal_profile[key]["time_sum"] += time * frequency
    temporal_profile[key]["time_count"] += frequency
    temporal_profile[key]["time_squares"] += (time ** 2) * frequency

for key in temporal_profile:
    data = temporal_profile[key]
    avg = data["time_sum"] / data["time_count"]
    variance = (data["time_squares"] / data["time_count"]) - (avg ** 2)
    stdev = statistics.sqrt(variance)
    temporal_profile[key] = (avg, stdev)

# Print the temporal profile
print(temporal_profile)
```

Please replace the `process_variants` list with the complete data from your question. The script calculates the average and standard deviation for each pair of activities and stores them in the `temporal_profile` dictionary.

Here's an example of how the output might look like:

```python
{
    ('Create Fine', 'Send Fine'): (8380516.026