Kumo-Anomaly

Diffusion-based anomaly detection and fine-tuning for multivariate time series. The model reconstructs randomly masked segments and scores each timestep by MAE between reconstruction and original signal; adaptive thresholding (SCS or MACS) converts scores to binary labels.

Source code: https://github.com/NVIDIA/Kumo-TS Pretrained weights: https://huggingface.co/nvidia/Kumo-Anomaly

For the most up-to-date usage information, refer to the README files in the Kumo-TS repository:

• Kumo-Anomaly/README.md — full SDK reference, model architecture, and API docs
• Kumo-Anomaly/examples/datasets/README.md — dataset format, synthetic data generation, and CSV conventions

External dependencies