MOMENT-1-large
| Property | Value |
|---|---|
| Parameter Count | 346M |
| License | MIT |
| Paper | ArXiv Link |
| Developed By | Auton Lab, Carnegie Mellon University & University of Pennsylvania |
What is MOMENT-1-large?
MOMENT-1-large is a powerful foundation model designed specifically for time-series analysis. As the largest variant in the MOMENT family, this 346M parameter model serves as a versatile tool for various time-series tasks, including forecasting, classification, anomaly detection, and imputation. What makes it particularly remarkable is its ability to perform effectively out-of-the-box, requiring minimal to no task-specific training data.
Implementation Details
The model is implemented using PyTorch and is available in F32 tensor format. It can be easily integrated into existing workflows using the momentfm package, supporting Python 3.11. The model architecture is based on transformers and has been trained on the AutonLab/Timeseries-PILE dataset.
- Supports multiple task configurations through simple API calls
- Includes built-in support for GPU acceleration and parameter efficient fine-tuning
- Provides comprehensive tutorials for each supported task type
Core Capabilities
- Zero-shot forecasting with configurable forecast horizons
- Few-shot classification for time series data
- Anomaly detection in temporal data streams
- Missing value imputation
- Representation learning for downstream tasks
Frequently Asked Questions
Q: What makes this model unique?
MOMENT-1-large stands out for its versatility and zero-shot capabilities. Unlike traditional time-series models that require extensive task-specific training, it can perform multiple tasks out-of-the-box while maintaining high accuracy. Its architecture allows for both direct use and fine-tuning for specific applications.
Q: What are the recommended use cases?
The model excels in various time-series applications, including financial forecasting, sensor data analysis, medical time-series (such as ECG classification), and industrial monitoring. It's particularly valuable when dealing with limited labeled data or when requiring multiple types of analysis on the same dataset.
