Chronos-Bolt
Part of the
light-curve family of open-source tools for astronomical time-series analysis.
Available from Python via the
light-curve package —
pip install light-curve.
Documentation:
https://light-curve.snad.space/
Paper
Ansari et al., 2024. "Chronos: Learning the Language of Time Series." Transactions
on Machine Learning Research. Chronos-Bolt is a faster, patch-based variant
released by Amazon Web Services.
1@article{ansari2024chronos,
2 title = {Chronos: Learning the Language of Time Series},
3 author = {Ansari, Abdul Fatir and others},
4 journal = {Transactions on Machine Learning Research},
5 year = {2024},
6 url = {https://github.com/amazon-science/chronos-forecasting},
7}
Original model
HuggingFace:
tiny ·
mini ·
small ·
basePackage: chronos-forecasting==2.3.0 (pip-installable, no code submodule)
Each size is pinned to a specific HF commit (see chronos_bolt_prep/config.py)
so the export is fully reproducible regardless of upstream changes to main.
License
Apache-2.0
Model overview
Chronos-Bolt is an encoder–decoder time series foundation model. Like Chronos 2,
it splits the input into non-overlapping patches of size 16, applies instance
normalization, and encodes the patches with a T5-style transformer. We export
only the encoder to produce embeddings; the four sizes differ only in width:
| Size | d_model |
|---|
| tiny | 256 |
| mini | 384 |
| small | 512 |
| base | 768 |
All four share an identical ONNX interface and preprocessing — the same as the
Chronos 2 export.
Irregular-sampling strategy
Following the
StarEmbed benchmark, timestamps
are
not passed to the model. Light curves are treated as equally spaced in
observation order; left-padding with NaN marks unused context positions.
Inputs
| Tensor | Shape | dtype | Description |
|---|
context | [batch, seq] | float32 | Magnitude values; NaN marks left-padded positions |
Both batch and seq are dynamic axes. seq must be a multiple of the
patch size (16) and may be anything up to the model's native context of 2048
(128 patches). Inference cost scales with seq, so shorter series are
proportionally cheaper — there is no fixed window and no truncation. Pad each
batch to a common multiple of 16 with NaN.
Outputs (ONNX)
One file per size, chronos-bolt-<size>.onnx, with two named outputs:
| Name | Shape | Description |
|---|
mean | [batch, d_model] | Masked mean pool over valid context patches |
sequence | [batch, seq/16, d_model] | Per-patch encoder hidden states |
Preprocessing steps
- Select observations (time-sorted magnitudes).
- Optionally cap to the last 2048 observations (the model's native context).
- Left-pad each series to a common length that is a multiple of 16, using NaN.
- Pass the
[batch, seq] float32 tensor to the ONNX model.
Instance normalisation (mean subtraction, std scaling) is applied internally by
the model.
Weights
Source: amazon/chronos-bolt-{tiny,mini,small,base} (loaded automatically via
ChronosBoltPipeline.from_pretrained, each pinned to a specific revision).
