TimesFM 2.0 500M
onlinegoogle/timesfm-2.0-500m-pytorch500M params | 512 context | $0.5000 input | $1.50 output
TimesFM 2.0 500M is Google's larger open TimesFM checkpoint for zero-shot time-series forecasting. It is a decoder-only patched transformer focused primarily on univariate point forecasting, with optional experimental quantile heads that Google notes are not calibrated after pretraining. It is the stronger open TimesFM variant when raw forecast accuracy matters more than footprint.
Model Classification
Family
TimesFM
Type
time series foundation model
Pretrained time-series model exposed on TSFM.ai for zero-shot or few-shot forecasting workloads.
Resources
Training Data
TimesFM family pretraining corpus plus an added LOTSA subset listed by the official model card, building on the broader Google Trends, Wikimedia Pageviews, and synthetic mixture used by the TimesFM line.
Recommended For
- • Long-context zero-shot forecasting with strong open-model baselines
- • Workloads where point forecasting quality matters more than broad task coverage
Strengths
- • Large open checkpoints with long context windows
- • Efficient patched-transformer design with strong zero-shot behavior
Limitations
- • Primarily a forecasting family rather than a general multi-task TSFM
- • Quantile support is not the main identity of the family
Capabilities
Tags
Specifications
- Parameters
- 500M
- Architecture
- decoder-only patched transformer (TimesFmModelForPrediction)
- Context length
- 512
- Max output
- 1,024
- Avg latency
- n/a
- Uptime
- n/a
- Rate limit
- n/a
- Accelerator
- NVIDIA GPU
- Regions
- Virginia, US
- License
- n/a
Pricing
- Input / 1M tokens
- $0.5000
- Output / 1M tokens
- $1.50
Performance
- Average latency
- n/a
- Availability
- n/a
- Rate limit
- n/a