Diffusion processes are great at smoothing out normal patterns while amplifying anomalies - perfect for AD.
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.
The second approach, "DiffusionAE," enhances the diffusion model's robustness to inherent noise in the data by utilizing the autoencoder's capability to pre-filter noise. The comprehensive methodology is depicted in a diagram that outlines the entire pipeline, from the introduction of noise to the generation of the anomaly score.
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