Forecast time-series data without training
timesfm-forecastingskillsetup L2★27,559
K-Dense-AI/scientific-agent-skills ↗What it does
Forecast time-series data using Google's TimesFM foundation model
Best for
Zero-shot forecasting without manual feature engineering or hyperparameter tuning.
Inputs
- · historical time-series data
- · forecast horizon
Outputs
- · point forecasts
- · confidence intervals
- · backtest metrics
Requires
- · TimesFM
- · NumPy
- · Hugging Face Transformers
Preconditions
Historical data minimum 512 points; stationarity check recommended
Failure modes
Out-of-distribution extrapolation; low forecast quality on volatile data
Trust signals
- · Google foundation model
- · pretrained weights
- · zero-shot capability