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license: apache-2.0
library_name: ts-arena
tags:
- time-series
- forecasting
- foundation-model
- chronos
- ts-arena
pipeline_tag: time-series-forecasting
---
# chronos_t5_tiny_8m_forecasting
TS Arena wrapper for Amazon Chronos T5-Tiny time series forecasting model.
## Model Description
Chronos is a family of pretrained time series forecasting models based on language model architectures.
It tokenizes time series values using scaling and quantization, then uses a T5 model to generate probabilistic forecasts.
| Attribute | Value |
|-----------|-------|
| **Parameters** | 8M |
| **Architecture** | T5 Encoder-Decoder |
| **Original Repo** | [amazon/chronos-t5-tiny](https://huggingface.co/amazon/chronos-t5-tiny) |
| **Paper** | [Chronos: Learning the Language of Time Series](https://arxiv.org/abs/2403.07815) |
| **Task** | Time Series Forecasting |
## Usage with TS Arena
```python
import ts_arena
# Load model
model = ts_arena.load_model("chronos-t5-tiny")
# Generate forecasts
import numpy as np
context = np.random.randn(96) # 96 timesteps of history
output = model.predict(context, prediction_length=24, num_samples=20)
# Access results
print(output.predictions.shape) # Point forecasts (median)
print(output.quantiles[0.5].shape) # Median forecast
print(output.quantiles[0.1].shape) # 10th percentile
print(output.quantiles[0.9].shape) # 90th percentile
```
## Direct Usage with Chronos
```python
from chronos import ChronosPipeline
import torch
pipeline = ChronosPipeline.from_pretrained(
"amazon/chronos-t5-tiny",
device_map="cuda",
torch_dtype=torch.bfloat16,
)
context = torch.randn(1, 96) # (batch, time)
forecast = pipeline.predict(context, prediction_length=24, num_samples=20)
```
## Evaluation Results
### ETTh1 Dataset (context=96, horizon=96)
| Metric | Value |
|--------|-------|
| MSE | 4.37 |
| MAE | 1.66 |
| RMSE | 2.09 |
## Features
- **Zero-shot forecasting**: No training required
- **Probabilistic forecasts**: Returns samples and quantiles
- **Variable horizons**: Supports different prediction lengths
- **Multivariate support**: Processes each channel independently
## Limitations
- Univariate model (multivariate handled channel-by-channel)
- No exogenous variable support
- Recommended max prediction length: 64
## Citation
```bibtex
@article{ansari2024chronos,
title={Chronos: Learning the Language of Time Series},
author={Ansari, Abdul Fatir and Stella, Lorenzo and Turkmen, Caner and Zhang, Xiyuan and others},
journal={arXiv preprint arXiv:2403.07815},
year={2024}
}
```
## License
Apache-2.0 (following the original Chronos license)
## Links
- [TS Arena GitHub](https://github.com/your-username/ts_arena)
- [Original Chronos](https://github.com/amazon-science/chronos-forecasting)
- [Chronos Paper](https://arxiv.org/abs/2403.07815)
|