RAMSeS: Robust and Adaptive Model Selection for Time-Series Anomaly Detection Algorithms
This work addresses the challenge of context-dependent anomaly detection in time-series data for domains like finance or IoT, but it is incremental as it builds on existing ensemble and selection techniques.
The paper tackles the problem of selecting effective anomaly detection algorithms for time-series data across diverse domains by introducing the RAMSeS framework, which uses a dual-branch approach with ensemble optimization and adaptive model selection to outperform prior methods on F1 scores.
Time-series data vary widely across domains, making a universal anomaly detector impractical. Methods that perform well on one dataset often fail to transfer because what counts as an anomaly is context dependent. The key challenge is to design a method that performs well in specific contexts while remaining adaptable across domains with varying data complexities. We present the Robust and Adaptive Model Selection for Time-Series Anomaly Detection RAMSeS framework. RAMSeS comprises two branches: (i) a stacking ensemble optimized with a genetic algorithm to leverage complementary detectors. (ii) An adaptive model-selection branch identifies the best single detector using techniques including Thompson sampling, robustness testing with generative adversarial networks, and Monte Carlo simulations. This dual strategy exploits the collective strength of multiple models and adapts to dataset-specific characteristics. We evaluate RAMSeS and show that it outperforms prior methods on F1.