用物理约束的扩散模型提升多变量时间序列异常检测效果
Physics-Informed Diffusion Models for Unsupervised Anomaly Detection in Multivariate Time Series
- 训练时引入加权物理损失，让模型学习数据的物理依赖动态
- 在合成与真实数据上均提升F1分数，生成数据多样性更好
- 适合需要高精度异常检测的工业时序场景
我们提出一种基于物理信息扩散模型的无监督多变量时间序列异常检测方法。近年来，扩散模型在时间序列预测、填补、生成和异常检测中表现优异。本文提出在扩散模型训练中使用静态权重调度的加权物理信息损失，以学习多变量时间序列数据的物理相关时间分布。该方法使扩散模型更准确地逼近底层数据分布，从而提升无监督异常检测性能。在合成与真实数据集上的实验表明，物理信息训练显著提升了异常检测的F1分数，同时改善了生成数据的多样性和对数似然。所提模型在合成数据集和一个真实数据集上优于基线方法及先前物理信息工作，在其他数据集上保持竞争力。
原文摘要 · Abstract (English)
We propose an unsupervised anomaly detection approach based on a physics-informed diffusion model for multivariate time series data. Over the past years, diffusion model has demonstrated its effectiveness in forecasting, imputation, generation, and anomaly detection in the time series domain. In this paper, we present a new approach for learning the physics-dependent temporal distribution of multivariate time series data using a weighted physics-informed loss during diffusion model training. A weighted physics-informed loss is constructed using a static weight schedule. This approach enables a diffusion model to accurately approximate underlying data distribution, which can influence the unsupervised anomaly detection performance. Our experiments on synthetic and real-world datasets show that physics-informed training improves the F1 score in anomaly detection; it generates better data diversity and log-likelihood. Our model outperforms baseline approaches, additionally, it surpasses prior physics-informed work and purely data-driven diffusion models on a synthetic dataset and one real-world dataset while remaining competitive on others.
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