MIXAD通过记忆网络实现可解释的时间序列异常检测
MIXAD: Memory-Induced Explainable Time Series Anomaly Detection
- 用记忆网络结合时空模块捕捉传感器间动态关系
- 通过记忆激活模式变化检测异常，提升可解释性
- 在可解释性指标上优于现有方法34.51%
在现代工业应用中，准确检测和诊断多变量时间序列数据中的异常至关重要。尽管存在这一需求，大多数前沿方法仍更侧重于检测性能而忽视模型可解释性。为填补这一空白，我们提出MIXAD（Memory-Induced Explainable Time Series Anomaly Detection），一种面向可解释异常检测的模型。MIXAD利用记忆网络与时空处理单元，理解传感器关系中固有的复杂动态与拓扑结构。我们还提出一种新型异常评分方法，通过检测异常期间记忆激活模式的显著变化来识别异常。该方法不仅保持良好的检测性能，且在可解释性指标上分别优于现有最优基线34.30%和34.51%。
原文摘要 · Abstract (English)
For modern industrial applications, accurately detecting and diagnosing anomalies in multivariate time series data is essential. Despite such need, most state-of-the-art methods often prioritize detection performance over model interpretability. Addressing this gap, we introduce MIXAD (Memory-Induced Explainable Time Series Anomaly Detection), a model designed for interpretable anomaly detection. MIXAD leverages a memory network alongside spatiotemporal processing units to understand the intricate dynamics and topological structures inherent in sensor relationships. We also introduce a novel anomaly scoring method that detects significant shifts in memory activation patterns during anomalies. Our approach not only ensures decent detection performance but also outperforms state-of-the-art baselines by 34.30% and 34.51% in interpretability metrics.
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