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Chronos2Forecaster

Chronos2Forecaster

class Chronos2Forecaster(model_path: str = 'amazon/chronos-2', config: dict = None, seed: int | None = None, ignore_deps: bool = False)[source]

Interface to the Chronos-2 Zero-Shot Forecaster by Amazon Research.

Chronos-2 is a pretrained encoder-only time series foundation model developed by Amazon for zero-shot forecasting. It supports univariate, multivariate, and covariate-informed forecasting tasks within a single architecture. The official code and technical report are given at [1] and [2].

Unlike Chronos (v1), Chronos-2 natively handles multivariate targets, past-only covariates, and known-future covariates via a group attention mechanism described in [2].

Parameters:
model_pathstr, default=”amazon/chronos-2”

Path to the Chronos-2 HuggingFace model.

configdict, optional, default=None

Configuration overrides. Supported keys:

  • “limit_prediction_length”bool, default=False

    If True, raises an error when prediction_length exceeds the model’s maximum prediction length.

  • “torch_dtype”torch.dtype, default=torch.bfloat16

    Data type for model weights and operations.

  • “device_map”str, default=”cpu”

    Device for inference, e.g., “cpu”, “cuda”, or “mps”.

  • “batch_size”int, default=256

    Number of time series per batch during prediction.

  • “context_length”int or None, default=None

    Maximum context length for inference. Defaults to model’s context length (8192 for amazon/chronos-2).

  • “cross_learning”bool, default=False

    If True, enables cross-learning across all input series in a batch, sharing information via the group attention mechanism.

seedint or None, optional, default=None

Random seed for reproducibility.

ignore_depsbool, optional, default=False

If True, dependency checks are skipped.

Attributes:
model_pipelineChronos2Pipeline

The underlying model pipeline used for forecasting.

References

[2] (1,2)

Abdul Fatir Ansari and others (2025). Chronos-2: Towards a Universal, General-Purpose Forecasting Foundation Model.

Examples

>>> from sktime.datasets import load_airline
>>> from sktime.forecasting.chronos2 import Chronos2Forecaster
>>> from sktime.split import temporal_train_test_split
>>> y = load_airline()
>>> y_train, y_test = temporal_train_test_split(y)
>>> forecaster = Chronos2Forecaster("amazon/chronos-2")
>>> forecaster.fit(y_train)
>>> y_pred = forecaster.predict(fh=[1, 2, 3])

Methods

check_is_fitted([method_name])

Check if the estimator has been fitted.

clone()

Obtain a clone of the object with same hyper-parameters and config.

clone_tags(estimator[, tag_names])

Clone tags from another object as dynamic override.

create_test_instance([parameter_set])

Construct an instance of the class, using first test parameter set.

create_test_instances_and_names([parameter_set])

Create list of all test instances and a list of names for them.

fit(y[, X, fh])

Fit forecaster to training data.

fit_predict(y[, X, fh, X_pred])

Fit and forecast time series at future horizon.

get_class_tag(tag_name[, tag_value_default])

Get class tag value from class, with tag level inheritance from parents.

get_class_tags()

Get class tags from class, with tag level inheritance from parent classes.

get_config()

Get config flags for self.

get_fitted_params([deep])

Get fitted parameters.

get_param_defaults()

Get object's parameter defaults.

get_param_names([sort])

Get object's parameter names.

get_params([deep])

Get a dict of parameters values for this object.

get_pretrained_params([deep])

Get pretrained parameters of this estimator.

get_tag(tag_name[, tag_value_default, ...])

Get tag value from instance, with tag level inheritance and overrides.

get_tags()

Get tags from instance, with tag level inheritance and overrides.

get_test_params([parameter_set])

Return testing parameter settings for the estimator.

is_composite()

Check if the object is composed of other BaseObjects.

load_from_path(serial)

Load object from file location.

load_from_serial(serial)

Load object from serialized memory container.

predict([fh, X])

Forecast time series at future horizon.

predict_interval([fh, X, coverage])

Compute/return prediction interval forecasts.

predict_proba([fh, X, marginal])

Compute/return fully probabilistic forecasts.

predict_quantiles([fh, X, alpha])

Compute/return quantile forecasts.

predict_residuals([y, X])

Return residuals of time series forecasts.

predict_var([fh, X, cov])

Compute/return variance forecasts.

pretrain(y[, X, fh])

Pre-train forecaster on panel (global) data.

reset()

Reset the object to a clean post-init state.

save([path, serialization_format])

Save serialized self to bytes-like object or to (.zip) file.

score(y[, X, fh])

Scores forecast against ground truth, using MAPE (non-symmetric).

set_config(**config_dict)

Set config flags to given values.

set_params(**params)

Set the parameters of this object.

set_random_state([random_state, deep, ...])

Set random_state pseudo-random seed parameters for self.

set_tags(**tag_dict)

Set instance level tag overrides to given values.

update(y[, X, update_params])

Update cutoff value and, optionally, fitted parameters.

update_predict(y[, cv, X, update_params, ...])

Make predictions and update model iteratively over the test set.

update_predict_single([y, fh, X, update_params])

Update model with new data and make forecasts.