面向多元时间序列无监督异常检测的物理信息扩散模型
Physics-Informed Diffusion Models for Unsupervised Anomaly Detection in Multivariate Time Series
AI总结：
本文提出一种基于物理信息扩散模型的多元时间序列无监督异常检测方法，通过加权物理信息损失学习物理依赖的时间分布，实验表明其在F1分数、数据多样性和对数似然上优于基线及纯数据驱动扩散模型。
摘要：
我们提出了一种基于物理信息扩散模型的多元时间序列数据无监督异常检测方法。在过去几年中，扩散模型已在时间序列领域的预测、插补、生成和异常检测中展现出有效性。本文提出了一种新方法，通过在扩散模型训练期间使用加权物理信息损失来学习多元时间序列数据依赖物理的时间分布。加权物理信息损失采用静态权重调度构建。该方法使扩散模型能够准确逼近底层数据分布，从而影响无监督异常检测性能。我们在合成数据集和真实世界数据集上的实验表明，物理信息训练提高了异常检测中的F1分数；它生成了更好的数据多样性和对数似然。我们的模型优于基线方法，此外，在一个合成数据集和一个真实世界数据集上超越了先前的物理信息工作以及纯数据驱动的扩散模型，同时在其他数据集上保持竞争力。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
