diff --git a/docs/source/images/chronos2_dark.svg b/docs/source/images/chronos2_dark.svg
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diff --git a/docs/source/images/chronos2_light.svg b/docs/source/images/chronos2_light.svg
new file mode 100644
index 0000000000..ce4b82925b
--- /dev/null
+++ b/docs/source/images/chronos2_light.svg
@@ -0,0 +1,3202 @@
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diff --git a/sdm/models/chronos2/model.py b/sdm/models/chronos2/model.py
index 2498c326fd..2841436257 100644
--- a/sdm/models/chronos2/model.py
+++ b/sdm/models/chronos2/model.py
@@ -43,10 +43,42 @@
class Chronos2(ICLModel):
- """The time series forecasting model from the
+ """The time series forecasting foundation model from the
`"Chronos-2: From Univariate to Universal Forecasting"
`_ paper.
+ .. figure:: /images/chronos2_light.svg
+ :figclass: light-only
+ :width: 100%
+
+ .. figure:: /images/chronos2_dark.svg
+ :figclass: dark-only
+ :width: 100%
+
+ :class:`Chronos2` is a time-series foundation model for multivariate
+ forecasting. It extends `Chronos `_ with
+ native support for jointly forecasting multiple coevolving target series,
+ incorporating historical covariates, and using dynamic covariates that are
+ known across both the past and future forecast horizon.
+
+ Architecturally, it combines patch-based time-series tokenization with
+ alternating temporal attention and group attention, allowing forecasts to
+ use both within-series history and information shared across targets and
+ covariates. Each patch encodes scaled values, time indices, and observation
+ masks. It returns 21 quantile forecasts: the 1st and 99th percentiles and
+ every fifth percentile from the 5th to the 95th, for each target series
+ and query time step. Forecasts of up to 1,024 time steps are decoded in
+ a single forward pass; longer horizons use autoregressive rollout.
+
+ Within the :class:`~sdm.models.ICLModel` protocol, :class:`Chronos2`
+ treats rows as ordered time steps and uses up to 8,192 time steps as
+ context. In a default forward pass, ``x_context`` contains historical
+ covariates, ``y_context`` contains one or more past target series, and
+ ``x_query`` contains future-known covariates (which must also be present in
+ ``x_context``). By default, examples in a batch are forecasted
+ independently; ``cross_learning=True`` allows group attention to share
+ information across examples in the batch.
+
Args:
pretrained: Whether to load the pretrained checkpoint.
device: The device.
diff --git a/sdm/models/timesfm3/model.py b/sdm/models/timesfm3/model.py
index de4f7fa464..a4fed7dad6 100644
--- a/sdm/models/timesfm3/model.py
+++ b/sdm/models/timesfm3/model.py
@@ -56,9 +56,9 @@ class TimesFM3(ICLModel):
:figclass: dark-only
:width: 100%
- :class:`TimesFM3` is a zero-shot time-series foundation model for
- multivariate forecasting. It extends earlier univariate TimesFM models with
- native support for jointly forecasting multiple coevolving target series,
+ :class:`TimesFM3` is a time-series foundation model for multivariate
+ forecasting. It extends earlier univariate TimesFM models with native
+ support for jointly forecasting multiple coevolving target series,
incorporating historical covariates, and using dynamic covariates that are
known across both the past and future forecast horizon.