timesfm-forecasting

Zero-shot time series forecasting with prediction intervals is generated by applying pre-trained models to univariate data, returning point estimates and quantile intervals without requiring custom model training. This Skill processes array or CSV-style inputs to produce uncertainty-aware future estimates.

Forecasting sales demand, sensor readings, energy usage, vitals, and weather data is possible without training a custom model. The zero-shot approach applies directly to univariate time series inputs to produce point forecasts and calibrated quantile-based prediction intervals.

Local inference for zero-shot forecasting requires sufficient RAM, GPU, and disk space to avoid crashes. A mandatory preflight system requirements checker runs first to verify that hardware resources meet the minimum thresholds before executing the forecasting task.

Preparing CSV data for univariate time series forecasting involves structuring inputs as array or CSV-style formats compatible with the forecasting script. An optional end-to-end CSV workflow example is provided to process inputs and export the resulting point forecasts and quantile intervals.

Time series forecasting scripts crash on insufficient system resources when RAM, GPU, or disk space fall below required thresholds. The preflight system requirements checker mitigates this by verifying available hardware resources before attempting local inference.

Quantile intervals improve time series forecasting results by providing probabilistic outputs alongside point forecasts, enabling uncertainty-aware decisions. Calibrated quantile-based prediction intervals define the range within which future values are expected to fall, rather than offering single point estimates.