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By k-dense-ai

timesfm-forecasting

k-dense-ai1.7k installs

Forecast your time series without model training.

What it does

Produces zero-shot time-series forecasts with quantile bands and guides held-out evaluation using TimesFM.

When to use it

Use for forecasting an ordered temporal target without training a task-specific model, not for causal estimation, clinical detection or physics-based climate modeling.

How to use it

Give it a time-series CSV and forecast horizon; it writes forecasts and a metadata sidecar to local files.

What you provide

  • the forecast horizon
  • Your files

Uses

Latest review

What you need · 8

Requires Python 3.10+ for the main workflow; the optional XReg workflow with current JAX requires Python 3.12+.

Uses uv to create the separate environment and install the reviewed packages.


Access · 4

This skill

google/timesfm-2.5-200m-pytorch

Read

Downloads pretrained TimesFM weights

uv pip install

Execute

Installs the forecasting dependencies