MIRAForecaster

MIRAForecaster

class MIRAForecaster(model_path: str = 'MIRA-Mode/MIRA', revision: str = 'main', config: dict | None = None, context_length: int | None = None, time_alpha: float = 1.0, time_snap_step: float = 0.1)[source]

Zero-shot forecaster wrapping Microsoft MIRA via vendored sktime.libs.mira.

MIRA is a foundation model for medical time series, supporting zero-shot forecasting on irregularly sampled clinical signals via continuous-time rotary positional encoding and neural ODE extrapolation.

Univariate only. Inference follows the autoregressive loop in the official MIRA repository [2].

Parameters:

model_pathstr, default=”MIRA-Mode/MIRA”

Hugging Face model id or local path.

revisionstr, default=”main”

Model revision on the Hugging Face Hub.

configdict, optional, default=None

Extra kwargs for MIRAForPrediction.from_pretrained.

context_lengthint, optional, default=None

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

time_alphafloat, default=1.0

alpha used for CT-RoPE normalization of time values.

time_snap_stepfloat, default=0.1

snap_step used for CT-RoPE normalization of time values.

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/MIRA-Mode/MIRA

[2]

https://github.com/microsoft/MIRA

[3]

https://arxiv.org/abs/2506.07584

Examples

>>> from sktime.datasets import load_airline
>>> from sktime.forecasting.mira import MIRAForecaster
>>> from sktime.split import temporal_train_test_split
>>> y = load_airline()
>>> y_train, _ = temporal_train_test_split(y)
>>> f = MIRAForecaster()
>>> 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.