tao-finetune-kumo-forecast

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

git clone --branch main --single-branch https://github.com/NVIDIA/Kumo-TS Kumo-TS
cd Kumo-TS/Kumo-Forecast
uv sync --group dev
uv pip install -e .          # editable install — required for clean sdk.* imports

# Standard inference (auto-downloads weights from HF on first run, no auth needed)
uv run python sdk/quick_example.py

Inference

Import and call perform_forecasting from sdk/forecasting.py. It auto-downloads weights, standardizes input, runs autoregressive rollout for long horizons, and returns a DataFrame with {target_column}_forecast rows for the requested horizon.

import sys, pandas as pd
sys.path.append("/path/to/Kumo-TS/Kumo-Forecast")  # clone Kumo-TS with --branch main
from sdk.forecasting import ForecastingConfig, perform_forecasting

df = pd.read_csv("your_data.csv")   # must have timestamp + numeric target column

config = ForecastingConfig(
    timestamp_column="timestamp",    # parseable datetime column
    target_column="target",          # primary target to forecast
    seq_len=512,                     # input context length (rows consumed)
    forecast_horizon=72,             # steps ahead to predict (max 512)
    model_horizon=72,                # native model horizon; change when using custom weights
    standardizer_pkl="standardizer.pkl",   # auto-downloaded from HF if missing
    ckpt="run8_best_model_cr.pt",          # auto-downloaded; see Checkpoints table
)
results = perform_forecasting(df=df, config=config)
# Returns DataFrame: timestamp | {target_column}_forecast  (forecast_horizon rows)
print(results.head())