Agent skill · nvidia
tao-finetune-kumo-forecast
Kumo-Forecast — transformer-based multivariate time series forecasting with DARR (context-enhanced kNN retrieval), interpretability, and fine-tuning. Use when the user asks to "forecast with Kumo-Forecast", "run forecasting inference", "use perform_forecasting", "DARR mode", "context-enhanced forecasting", "lag horizon attribution", "interpretability", "fine-tune forecasting", "fine-tune forecasting with automl", "hyper-parameter optimization with forecasting", or mentions "kumo-forecast", "moment_head_512_6hr", or "run8_best_model_cr".
What it needs
About 7k tokens when loaded.
What this skill does
Kumo-Forecast Transformer-based multivariate time series forecasting using self-supervised pretraining on diverse temporal data. Three inference modes: standard (direct forecast), DARR (context-enhanced kNN retrieval blending), and interpretability (latent trajectory extraction, semantic flow, lag×horizon attribution, trajectory stability, and diagnostic ratios — full explanation bundle with PDF report). Fine-tuning adapts the forecasting head — and optionally the cross-channel layer — to your domain. Source code: https://github.com/NVIDIA/Kumo-TS Pretrained weights: https://huggingface.co/nvidia/Kumo-Forecast External dependencies Dependency Purpose Install --- --- --- Python 3.10+ Runtime https://www.python.org/downloads/ uv Package + environment manager pip install uv CUDA toolkit (optional) GPU acceleration https://developer.nvidia.com/cuda-downloads matplotlib (optional) Interpretability PDF report, heatmap PNG, flow + stability charts uv add matplotlib Credentials nvidia/Kumo-Forecast is a public repo — no token required for downloading weights. If you hit a 401/403 (gated access or license not accepted) or a 504 on first download, see the Known pitfalls section. Quick start Inference Import and call performforecasting from sdk/forecasting.py. It auto-downloads weights, standardizes input, runs autoregressive rollout for long horizons, and returns a DataFrame with {targetcolumn}forecast rows for the requested horizon. For regular inference, put forecasting arguments in YAML and pass the file as config. Start from the fully commented template in sdk/forecastinginferenceconfig.yaml. The YAML can be flat or use a top-level inference: section inside a larger TAO/AutoML spec. Unknown keys raise ValueError. …
How to use it
Reference it in AdaL, Claude Code, Cursor or any coding agent — nothing to install:
@skills nvidia/tao-finetune-kumo-forecast