Skip to content
New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

About anomaly detection #27

Open
donghaopeng123 opened this issue Jul 8, 2024 · 1 comment
Open

About anomaly detection #27

donghaopeng123 opened this issue Jul 8, 2024 · 1 comment

Comments

@donghaopeng123
Copy link

Hello, thank you very much for providing such an excellent idea and implementation. However, the performance of my anomaly detection run has not reached the level stated in your paper. Could you please offer me some suggestions? The dataset I'm currently using is the SMD dataset. My F1 score is 81.81, which is quite a bit lower than the 88.09 mentioned in your paper. If I want to achieve results similar to yours, what parts of the code should I adjust? I would greatly appreciate any advice you can give.
The experimental environment is Ubuntu 22.04.3 LTS operating system, with an Intel® Xeon® CPU E5-2609 v4 @ 1.70GHz, two NVIDIA GeForce RTX 4090 GPUs, and 173GB of RAM.

@gasvn
Copy link
Member

gasvn commented Jul 8, 2024

For anomaly detection task on a single task, we follow existing works to do parameter sweeps to get the best setting. Here is the parameter and training log. It's possible that the results are not identical to the paper since results from these datasets in time series are not very stable.
output.log

Sign up for free to join this conversation on GitHub. Already have an account? Sign in to comment
Labels
None yet
Projects
None yet
Development

No branches or pull requests

2 participants