light-curve/chronos2

Chronos 2

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: light-curve.snad.space.

Paper

Ansari et al., 2025. "Chronos-2: Learning the Language of Time Series." Amazon Web Services.

@misc{ansari2025chronos2,
  title   = {Chronos-2: Learning the Language of Time Series},
  author  = {Abdul Fatir Ansari and others},
  year    = {2025},
  url     = {https://huggingface.co/amazon/chronos-2},
}

Original model

HuggingFace: https://huggingface.co/amazon/chronos-2

Package: chronos-forecasting==2.3.0 (pip-installable, no code submodule)

Weights are pinned to commit 29ec3766 of the HF repo, so the export is fully reproducible regardless of upstream changes to main.

License

Apache 2.0

Model overview

Chronos 2 is a 120 M-parameter encoder-only time series foundation model trained on a large corpus of real and synthetic time series data. It maps a sequence of scalar observations into a sequence of patch embeddings via a T5-style transformer encoder. The architecture uses non-overlapping patches of size 16 and instance normalisation with arcsinh transformation.

Irregular-sampling strategy

Following the StarEmbed benchmark, timestamps are not passed to Chronos 2. The model encodes relative position implicitly via sequential patch indices (scaled by the context length), so light curves are treated as equally spaced in observation order. Left-padding with NaN marks unused context positions.

Passband encoding

Chronos 2's embed() API accepts only magnitude values; there is no built-in passband channel. Multi-band data can be embedded per-band separately, or stacked as multiple variates (shape [n_bands, seq_len]). Explicit lg(λ) injection would require direct calls to model.encode() with a custom input tensor — the architecture has no dedicated wavelength embedding layer.

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 8192 (512 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)

Name	Shape	Description
mean	[batch, 768]	Masked mean pool over valid context patches
sequence	[batch, seq/16, 768]	Per-patch encoder hidden states

Preprocessing steps

1. Select observations (time-sorted magnitudes).
2. Optionally cap to the last 8192 observations (the model's native context).
3. Left-pad each series to a common length that is a multiple of 16, using NaN.
4. Pass the [batch, seq] float32 tensor to the ONNX model.

Instance normalisation (mean subtraction, std scaling, arcsinh) is applied internally by the model.

Weights

Source: https://huggingface.co/amazon/chronos-2 (loaded automatically via Chronos2Pipeline.from_pretrained, pinned to revision 29ec3766d36d6f73f0696f85560a422f50e8498c)