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[2602.21766] RAMSeS: Robust and Adaptive Model Selection for Time-Series Anomaly Detection Algorithms
Summary
The RAMSeS framework enhances time-series anomaly detection by combining a stacking ensemble with adaptive model selection, optimizing performance across diverse datasets.
Why It Matters
As time-series data varies significantly across domains, a universal anomaly detection method is often ineffective. RAMSeS addresses this challenge by providing a robust and adaptable solution that improves detection accuracy, making it relevant for industries reliant on time-series analysis, such as finance, healthcare, and IoT.
Key Takeaways
- RAMSeS combines a stacking ensemble and adaptive model selection for improved anomaly detection.
- The framework uses genetic algorithms and techniques like Thompson sampling for optimal model performance.
- RAMSeS outperforms existing methods on F1 score, showcasing its effectiveness across diverse datasets.
Computer Science > Databases arXiv:2602.21766 (cs) [Submitted on 25 Feb 2026] Title:RAMSeS: Robust and Adaptive Model Selection for Time-Series Anomaly Detection Algorithms Authors:Mohamed Abdelmaksoud, Sheng Ding, Andrey Morozov, Ziawasch Abedjan View a PDF of the paper titled RAMSeS: Robust and Adaptive Model Selection for Time-Series Anomaly Detection Algorithms, by Mohamed Abdelmaksoud and 3 other authors View PDF HTML (experimental) Abstract: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. Subjects: Databases (cs.DB); Machine Learning (cs.LG)...
