Revisiting OmniAnomaly for Anomaly Detection: performance metrics and comparison with PCA-based models

This paper compares OmniAnomaly, a popular deep learning model for detecting anomalies in time series data from multiple sources, against a simpler traditional statistical method called PCA. Using the same fair evaluation standards across 100 runs, the researchers found that the simpler PCA method performs just as well or even better than the complex deep learning model, suggesting that how we measure and compare these models matters more than we thought.