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.

翻译：时间序列数据在不同领域间差异显著，使得通用的异常检测器难以实现。在某一数据集上表现优异的方法通常无法直接迁移，因为异常的定义高度依赖于具体场景。核心挑战在于设计一种方法，既能针对特定情境取得优异性能，又能适应不同数据复杂度的跨领域应用。本文提出面向时间序列异常检测的鲁棒自适应模型选择框架RAMSeS。该框架包含两个分支：(i) 通过遗传算法优化的堆叠集成分支，用于融合互补检测器的优势；(ii) 自适应模型选择分支，通过汤普森采样、生成对抗网络的鲁棒性测试及蒙特卡洛模拟等技术，识别最佳单一检测器。这种双重策略既利用了多模型的集体优势，又能适应数据集的特定特征。实验评估表明，RAMSeS在F1分数上优于现有方法。