Physics-Informed Diffusion Models for Unsupervised Anomaly Detection in Multivariate Time Series
This work addresses anomaly detection for time series data, offering an incremental improvement by integrating physics constraints into diffusion models.
The paper tackles unsupervised anomaly detection in multivariate time series by proposing a physics-informed diffusion model with a weighted loss during training, which improves F1 scores and outperforms baselines on synthetic and real-world datasets.
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.