提出一种主动检测时间序列异常的新方法，提前预警故障。
Possibility for Proactive Anomaly Detection
- 基于预测模型与数据驱动阈值，提前识别异常
- 在四个基准上验证，可检测可预测和不可预测异常
- 适合需要提前干预的工业监控场景
时间序列异常检测是实际应用中减少潜在损失的重要技术。现有方法依赖模型输出与真实值之间的误差，难以实用。本文提出一种主动检测方法，结合专为异常检测设计的时间序列预测模型与数据驱动的异常检测模型。通过训练数据建立异常阈值，后续通过判断预测值是否超过阈值来检测异常。我们在四个异常检测基准上进行了全面评估，并分析了可预测与不可预测异常的表现。源代码已作为补充材料提供。
原文摘要 · Abstract (English)
Time-series anomaly detection, which detects errors and failures in a workflow, is one of the most important topics in real-world applications. The purpose of time-series anomaly detection is to reduce potential damages or losses. However, existing anomaly detection models detect anomalies through the error between the model output and the ground truth (observed) value, which makes them impractical. In this work, we present a \textit{proactive} approach for time-series anomaly detection based on a time-series forecasting model specialized for anomaly detection and a data-driven anomaly detection model. Our proactive approach establishes an anomaly threshold from training data with a data-driven anomaly detection model, and anomalies are subsequently detected by identifying predicted values that exceed the anomaly threshold. In addition, we extensively evaluated the model using four anomaly detection benchmarks and analyzed both predictable and unpredictable anomalies. We attached the source code as supplementary material.
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