Base ModelForecasting
TimesFM 2.5 · Time-Series Forecasting
Google's forecasting foundation model · test it on your series
Google Research · TimesFMApache-2.0
Use to forecast your own time series without training, with Google's TimesFM. Inference-only; for a trainable temporal model pick TCPFN.
Params
~230M
Context
1024 steps
Output
Quantiles
up to 256-step horizon
Run it
CLI
git clone https://github.com/profitopsai/pfnstudio && cd pfnstudio
pip install -e packages/core -e packages/cli
pfnstudio run timesfm-basePulls the pretrained weights from Hugging Face and runs the model's own held-out sample, printing the tasks it supports, a lock digest pinning exactly what was pulled, and a live result. Pass --input your.json to run your data.
Or in the studio: install this base model → then Predict.