Are diffusion models secretly OP at anomaly detection?

Diffusion processes are great at smoothing out normal patterns while amplifying anomalies

Are diffusion models better at finding time series anomalies than current state-of-the-art approaches?

Multivariate time series anomaly detection is critical in fields ranging from healthcare and finance to cybersecurity and industrial surveillance. Spotting these anomalies can highlight significant events such as health conditions, fraudulent activity, cyber threats, or equipment malfunctions. As IoT devices and high-frequency data collection become more prevalent, the need for robust anomaly detection models for multivariate time series has become essential.

Deep learning methods have made significant strides in this area. Autoencoders, Generative Adversarial Networks (GANs), and Transformers are just a few of the approaches that have demonstrated effectiveness in identifying anomalies within time series data. A recent piece I shared discussed the innovative application of "inverted transformers" (iTransformers) in time series analysis, which you can read more about here.

However, a new twist emerged with my latest find—a new research paper on the use of diffusion models for time series data analysis. These models are best known for their impressive results in image and audio generation tasks, as evidenced by Stable Diffusion for images and AudioLDM for audio. They've even been applied to help robots adapt to complex environments, which you can learn about here.

This raises a compelling question: Can diffusion models be as effective for analyzing time series data? This post will examine the recent paper that has brought this question to the forefront, and we'll assess the viability of diffusion models in this specialized domain. Let's get started.