Third-party cyber evaluations involving OpenAI models
openai.com·14h ago
AI Research
arXiv CS.AI·Jun 9·5 min read
PAI: Preserving Amplitude Information in Representation-Based Time-Series Anomaly Detection
TL;DR
Representation-based time-series anomaly detection methods often ignore amplitude information, which can lead to poor performance on anomalies related to amplitude. To tackle this issue, a new method called PAI (Preserving Amplitude Information) was developed to retain amplitude data in the learned embeddings.
✦ Why It Matters
Incorporating amplitude information can enhance the accuracy of anomaly detection in time-series data.
Key Takeaways
- Incorporate amplitude information in anomaly detection models.
- Use PAI's diagnostic module to evaluate representation quality.
- Combine median and MAD scores for improved anomaly scoring.
- Test PAI on your datasets to benchmark performance improvements.
How It Works
PAI operates through a two-module system. The diagnostic module evaluates the representation's ability to capture amplitude information by comparing cosine and Euclidean distances.
The final score augmentation function then integrates a point-wise median and MAD deviation score with a local mean-shift score, enhancing the overall anomaly score.