Chronos-T5-Small
| Property | Value |
|---|---|
| Parameter Count | 46.2M |
| Model Type | Time Series Forecasting |
| Architecture | T5-based Transformer |
| License | Apache-2.0 |
| Paper | Chronos: Learning the Language of Time Series |
What is chronos-t5-small?
Chronos-T5-Small is a specialized time series forecasting model that transforms numerical time series data into token sequences for prediction. Based on the efficient T5 architecture, it uses 4,096 tokens (compared to T5's 32,128) and has been trained on both public time series data and synthetic Gaussian process data. The model enables probabilistic forecasting by sampling multiple future trajectories based on historical context.
Implementation Details
The model employs a unique approach to time series forecasting by treating it as a language modeling task. It first scales and quantizes time series data into tokens, processes them through the T5 architecture, and generates probabilistic forecasts through sampling.
- 46.2M parameters optimized for time series prediction
- Based on T5-efficient-small architecture
- Uses cross-entropy loss during training
- Supports batch processing and GPU acceleration
Core Capabilities
- Probabilistic forecasting through multiple trajectory sampling
- Handles various time series lengths and patterns
- Efficient processing with bfloat16 support
- Integrated scaling and quantization of input data
Frequently Asked Questions
Q: What makes this model unique?
The model's distinctive feature is its approach to treating time series forecasting as a language modeling problem, using token-based representation and transformer architecture for probabilistic predictions.
Q: What are the recommended use cases?
This model is ideal for business forecasting, demand prediction, and any time series analysis requiring probabilistic forecasts. It's particularly useful when uncertainty estimation is important for decision-making.
