TimesFM-3
TimesFM-3 is a released general LLM work model with open-weight; evaluate it while provider pricing coverage matures.
- Family
- TimesFM
- Released
- 2026-08-31
- Parameters
- 330M
- Architecture
- Decoder Only
- Specialization
- forecasting
- Openness
- Open weights
- License
- NoncommercialCommercial use: non-commercial
- Weights
- Available
- Code
- Unknown
- Training
- Pretrained
No tracked provider pricing is available yet.
About
TimesFM-3 (TimesFM 3.0) is Google Research's zero-shot time-series foundation model for multivariate and univariate forecasting, released August 31, 2026. First-party blog: 330 million parameters, pretrained on more than 1 trillion real-world and synthetic time points; decoder-only transformer with 32-step patches, alternating causal temporal attention and full variate attention, and single-pass Contiguous Patch Masking. Natively supports multiple targets, past covariates, and past-future (dynamic) covariates, with point forecasts plus 9 quantiles (10th–90th percentile). Official PyTorch checkpoint google/timesfm-3.0-pytorch on Hugging Face. GitHub code is Apache-2.0; TimesFM 3.0 pretrained weights are TimesFM Non-Commercial License v1.0 (non-commercial, non-production).
Providers
No active provider routes are tracked for this model yet.
Capabilities
No model capability flags are currently sourced.
Benchmark peer barsfor Coding
No task-mapped benchmark peers are available for this model yet.
Migration checks
No linked migration route is available for this model yet.
API versions
timesfm-3No tracked provider pricing is available yet.