简单集成模型在工业时序异常检测中胜过复杂方法。
Segmentation over Complexity: Evaluating Ensemble and Hybrid Approaches for Anomaly Detection in Industrial Time Series
- 用分段数据训练随机森林+XGBoost集成模型
- AUC-ROC达0.976，F1-score为0.41，早期检测率100%
- 适合数据不平衡且时间不确定的工业场景
本研究探讨了高级特征工程与混合模型架构在多变量工业时序异常检测中的有效性，聚焦于蒸汽轮机系统。我们评估了基于变点的统计特征、聚类生成的子结构表示以及混合学习策略对检测性能的影响。尽管这些方法理论上有吸引力，但其表现始终不及在分段数据上训练的简单随机森林+XGBoost集成模型。该集成模型在定义的时间窗口内实现100%早期检测，获得0.976的AUC-ROC和0.41的F1-score。研究结果表明，在高度不平衡且时间不确定性高的场景下，模型简单性结合优化分段可超越复杂架构，具备更强鲁棒性、可解释性与实际应用价值。
原文摘要 · Abstract (English)
In this study, we investigate the effectiveness of advanced feature engineering and hybrid model architectures for anomaly detection in a multivariate industrial time series, focusing on a steam turbine system. We evaluate the impact of change point-derived statistical features, clustering-based substructure representations, and hybrid learning strategies on detection performance. Despite their theoretical appeal, these complex approaches consistently underperformed compared to a simple Random Forest + XGBoost ensemble trained on segmented data. The ensemble achieved an AUC-ROC of 0.976, F1-score of 0.41, and 100% early detection within the defined time window. Our findings highlight that, in scenarios with highly imbalanced and temporally uncertain data, model simplicity combined with optimized segmentation can outperform more sophisticated architectures, offering greater robustness, interpretability, and operational utility.
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