metadata
license: apache-2.0
library_name: ts-arena
tags:
- time-series
- forecasting
- foundation-model
- chronos
- chronos-bolt
- ts-arena
pipeline_tag: time-series-forecasting
chronos_bolt_mini_21m_forecasting
TS Arena wrapper for Amazon Chronos BOLT-Mini time series forecasting model.
Model Description
Chronos-Bolt is an optimized version of the Chronos family, designed for faster inference while maintaining accuracy. It uses an optimized T5 architecture with improved tokenization and reduced computational overhead.
| Attribute | Value |
|---|---|
| Parameters | 21M |
| Architecture | T5 Optimized (Bolt) |
| Original Repo | amazon/chronos-bolt-mini |
| Paper | Chronos: Learning the Language of Time Series |
| Task | Time Series Forecasting |
Usage with TS Arena
import ts_arena
# Load model
model = ts_arena.load_model("chronos-bolt-mini")
# 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
from chronos import ChronosPipeline
import torch
pipeline = ChronosPipeline.from_pretrained(
"amazon/chronos-bolt-mini",
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
@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)