AuroraForecaster

class AuroraForecaster(repo_id: str = 'DecisionIntelligence/Aurora', weights_filename: str = 'model.safetensors', cache_dir: str | None = None, force_download: bool = False, device: str | None = None, context_length: int | None = None, inference_token_len: int = 48, num_samples: int = 100, max_text_length: int = 200, text: str | None = None, vision=None)[source]

Zero-shot forecaster wrapping Aurora via the aurora-model package.

Aurora is a multimodal time series foundation model supporting generative probabilistic forecasting. Besides multivariate support, it can optionally condition on free-text domain context and vision inputs.

Inference follows the official aurora-model API [2] and Hugging Face model card [1] examples.

Parameters:

repo_idstr, default=”DecisionIntelligence/Aurora”

Hugging Face repository id for model weights.

weights_filenamestr, default=”model.safetensors”

Weights file name in the Hugging Face repository.

cache_dirstr, optional, default=None

Local cache directory for downloaded weights.

force_downloadbool, default=False

Whether to force re-download of weights from the Hub.

devicestr, optional, default=None

Device for inference. If None, uses CUDA when available, else CPU.

context_lengthint, optional, default=None

Number of trailing history steps passed to the model. If None, uses the full series seen at predict time.

inference_token_lenint, default=48

Patch length for inference. Using the series period length when known is recommended.

num_samplesint, default=100

Number of stochastic forecast trajectories from flow matching. Point forecasts use the sample mean. Quantile forecasts require num_samples > 1.

max_text_lengthint, default=200

Maximum token length when text is provided.

textstr, optional, default=None

Optional text context for multimodal forecasting (e.g. domain metadata or event descriptions). The same text is applied to every target variable (channel-independent inference).

visionPIL.Image, array-like, or torch.Tensor, optional, default=None

Optional external RGB image for multimodal conditioning. When None (default), Aurora renders a pseudo-image from the historical series internally (period-based 2D layout).

Set vision only when a real image should provide additional context alongside the series (e.g. a chart or domain photograph). Accepted inputs are passed to Aurora’s ViT preprocessor: a single PIL.Image, a list of images (batch size must match the model batch: 1 for univariate, n_vars for multivariate), a numpy array, or a torch.Tensor of shape (batch, 3, H, W) in RGB channel order. Images are resized to 224x224 and normalized inside the model.

Attributes:

cutoff: Cut-off = “present time” state of forecaster.
fh: Forecasting horizon that was passed.
is_fitted: Whether fit has been called.
state: State of the estimator.

References

[1]

https://huggingface.co/DecisionIntelligence/Aurora

[2]

https://pypi.org/project/aurora-model/0.2.0/

[3]

https://arxiv.org/abs/2509.22295

Examples

>>> from sktime.datasets import load_airline
>>> from sktime.forecasting.aurora import AuroraForecaster
>>> from sktime.split import temporal_train_test_split
>>> y = load_airline()
>>> y_train, _ = temporal_train_test_split(y)
>>> f = AuroraForecaster(num_samples=10)
>>> f.fit(y_train)
>>> y_pred = f.predict(fh=[1, 2, 3])

Multimodal forecasting with text context:

>>> f = AuroraForecaster(
...     text="Monthly airline passenger totals.",
...     num_samples=10,
... )
>>> f.fit(y_train)
>>> y_pred = f.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.