MOMENT-Small
onlineAutonLab/MOMENT-1-small~40M params | 512 context | $0.5000 input | $1.50 output | MIT
MOMENT-Small is the lightweight checkpoint in AutonLab's general-purpose time-series foundation-model family. Like the larger variants, it is framed as a multi-task representation model transferring across forecasting, classification, anomaly detection, imputation, and embedding extraction. The small variant is the most latency-friendly way to access the MOMENT architecture.
Model Classification
Family
MOMENT-1
Type
time series foundation model
Pretrained time-series model exposed on TSFM.ai for zero-shot or few-shot forecasting workloads.
Resources
Training Data
Timeseries-PILE, built from public forecasting, classification, and anomaly-detection corpora including Informer datasets, Monash, UCR/UEA, and TSB-UAD.
Recommended For
- • Shared backbones across forecasting, anomaly detection, classification, and imputation
- • Teams that want one general-purpose time-series representation model
Strengths
- • Broadest multi-task scope in the hosted catalog
- • Useful when the same deployment needs to cover several downstream tasks
Limitations
- • Not optimized purely around one forecasting leaderboard objective
- • May be heavier than needed if you only need straightforward zero-shot forecasting
Capabilities
forecastingclassificationanomaly-detectionimputationretrieval
Tags
momentmulti-taskrepresentation-learninglightweight
Specifications
- Parameters
- ~40M
- Architecture
- patch-based encoder-only transformer trained with masked time-series modeling
- Context length
- 512
- Max output
- 1,024
- Avg latency
- n/a
- Uptime
- n/a
- Plan limits
- 1,000 rpm free · 1,000,000 rpm with billing
- Accelerator
- NVIDIA GPU
- Regions
- Virginia, US
- License
- MIT
Pricing
- Input / 1M tokens
- $0.5000
- Output / 1M tokens
- $1.50
Performance
- Average latency
- n/a
- Availability
- n/a
- Plan limits
- 1,000 rpm free · 1,000,000 rpm with billing