Exploring the Potential of Diffusion Models in Time Series Anomaly Detection
Too Long; Didn't ReadDetecting anomalies in time series data is crucial in various domains, and deep learning methods have shown promise in this area. This article discusses the use of diffusion models for time series anomaly detection, a novel approach that involves gradually adding noise to data and then reversing it to enhance anomaly identification. The paper examines the performance of diffusion models on synthetic and real-world datasets, proposing advanced evaluation metrics to better assess their capabilities. While promising, these models face challenges with complex real-world data and require further research and optimization for practical applications.