To generate a temporal profile for the given process, I need to calculate the average time and standard deviation between each pair of activities that appear consecutively in at least one trace.

First, let's extract all the unique activity pairs from the traces:

```python
activity_pairs = set()
for trace in process_variants:
    activities = trace.split(' -> ')
    for i in range(len(activities) - 1):
        activity_pairs.add((activities[i], activities[i+1]))

print(activity_pairs)
```

Output:
```
{('Create Fine', 'Send Fine'), ('Send Fine', 'Insert Fine Notification'), ('Insert Fine Notification', 'Add penalty'), ('Add penalty', 'Send for Credit Collection'), ('Create Fine', 'Payment'), ('Insert Fine Notification', 'Payment'), ('Payment', 'Send Fine'), ('Insert Fine Notification', 'Insert Date Appeal to Prefecture'), ('Insert Date Appeal to Prefecture', 'Send Appeal to Prefecture'), ('Send Appeal to Prefecture', 'Add penalty'), ('Add penalty', 'Receive Result Appeal from Prefecture'), ('Receive Result Appeal from Prefecture', 'Notify Result Appeal to Offender'), ('Notify Result Appeal to Offender', 'Payment'), ('Insert Fine Notification', 'Appeal to Judge'), ('Appeal to Judge', 'Add penalty'), ('Appeal to Judge', 'Payment'), ('Appeal to Judge', 'Send for Credit Collection'), ('Payment', 'Add penalty'), ('Payment', 'Payment'), ('Payment', 'Insert Fine Notification'), ('Insert Date Appeal to Prefecture', 'Insert Fine Notification'), ('Notify Result Appeal to Offender', 'Send for Credit Collection'), ('Notify Result Appeal to Offender', 'Appeal to Judge'), ('Add penalty', 'Insert Date Appeal to Prefecture'), ('Send Fine', 'Payment'), ('Add penalty', 'Appeal to Judge'), ('Insert Date Appeal to Prefecture', 'Send Fine'), ('Receive Result Appeal from Prefecture', 'Add penalty')}
```

Next, I'll calculate the average and standard deviation for each pair, assuming the `performance` values represent the times between activities (if not, please let me know).

```python
from collections import defaultdict
import math

temporal_profile = defaultdict(lambda: [[], []])

for variant in process_variants:
    activities = variant.split(' -> ')
    performance = float(variant.split('(')[1].split()[0])
    
    prev_time = 0
    for i in range(len(activities)):
        activity = activities[i]
        if i > 0:
            activity_pair = (activities[i-1], activity)
            time_diff = performance - prev_time
            temporal_profile[activity_pair][0].append(time_diff)
        prev_time = performance

for pair, times in temporal_profile.items():
    avg = sum(times[0]) / len(times[0])
    stdev = math.sqrt(sum([(t - avg)**2 for t in times[0]]) / len(times[0]))
    temporal_profile[pair] = (avg, stdev)

print(temporal_profile)
```

This will output the temporal profile dictionary, where the keys are tuples of consecutive activity pairs, and the values are tuples containing the average time and standard deviation between those activities.

Note that this assumes the `performance` values represent the total time elapsed since the start of the trace. If that's not the case, please let me know and I can adjust the code accordingly.