PatchTST-FM R1
onlineibm-research/granite-timeseries-patchtst-fm-r1~325M params | 8K context | $0.00025 per forecast | Apache-2.0
PatchTST-FM R1 is IBM's foundation-model extension of PatchTST, trained for broad zero-shot forecasting rather than a single dataset. It features an 8192-token context window, a 99-quantile probabilistic output head, and simultaneous imputation-and-forecasting capabilities for series with missing values. IBM positions it as a top-5 replicable zero-shot model on the GIFT-Eval leaderboard as of March 2026.
Model Classification
Family
Granite PatchTST
Type
time series foundation model
Pretrained time-series model exposed on TSFM.ai for zero-shot or few-shot forecasting workloads.
Resources
Training Data
Broad multi-domain pretraining corpus for zero-shot generalization; IBM positions the model on the GIFT-Eval leaderboard without disclosing the full mixture.
Recommended For
- • Zero-shot probabilistic forecasting across diverse domains
- • Long-context forecasting with simultaneous imputation of missing values
Strengths
- • 8192-token context enables very long lookback windows
- • 99-quantile output provides rich uncertainty estimates without sampling
Limitations
- • Large model footprint requires GPU inference
- • Newer foundation-model release with less production track record than the original PatchTST baseline
Capabilities
Tags
Specifications
- Parameters
- ~325M
- Architecture
- PatchTST transformer with 8192-token context and 99-quantile output head
- Context length
- 8,192
- Max context
- 8,192
- Minimum history
- n/a
- Recommended history
- n/a
- Input step
- n/a
- Required target series
- 1
- Temperature
- Ignored
- Top P
- Ignored
- Max output
- 2,048
- Avg latency
- n/a
- Uptime
- n/a
- Plan limits
- 1,000 rpm free · 1,000,000 rpm with billing
- Accelerator
- L40S
- Regions
- Virginia, US
- License
- Apache-2.0
Pricing
- Per forecast
- $0.00025
Performance
- Average latency
- n/a
- Availability
- n/a
- Plan limits
- 1,000 rpm free · 1,000,000 rpm with billing