Individual skill
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".
skills/tao-finetune-kumo-forecast/SKILL.md
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- Last checked
- Nov 9, 2026
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- 3,933
- Repository last updated
- Nov 11, 2026
- License
- Apache-2.0
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- agent-skills Repository topic
- agentic-ai Repository topic
- ai-agents Repository topic
- claude-code Repository topic
- codex Repository topic
- coding-agents Repository topic
- cuda Repository topic
- developer-tools Repository topic
- llm Repository topic
- omniverse Repository topic
- forecasting Declared skill tag
- time-series Declared skill tag
- darr Declared skill tag
- interpretability Declared skill tag
- automl Declared skill tag
- finetune Declared skill tag
- inference Declared skill tag
- kumo-forecast Declared skill tag
- engineering Derived category
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# 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 |