I have a question for AI or data experts. I'm writing a paper

My dataset are time-series sensor data and anomaly ratio is between 5% and 6%

1.
For time-series anomaly detection evaluation, which one is better, *precision/recall/F1* or *ROC-AUC* ?

When empirically studying this issue, I found some papers use *precision/recall/F1* and some papers use *ROC-AUC* .

Considering that positive samples(anomalies) are relatively less than negative samples(normal points), which one is better?

I'm confused with this issue

2.
If I use *precision/recall/F1* , should I check *precision/recall/F1* only for positive class ?

I think because the number of positive samples are sparse, it's not appropriate to check *precision/recall/F1* only for positive class

Thus, should I check *precision/recall/F1* for both positive class and negative class?

If that's right, can I report *precision/recall/F1* with **macro avg** in my paper?

(you can see the picture below. I used classification\_report in sklearn library)

Thank you for your explanation !

[![enter image description here](https://i.stack.imgur.com/LEVGb.png)](https://i.stack.imgur.com/LEVGb.png)