MantisForecaster

class MantisForecaster(checkpoint='paris-noah/MantisV2', model_version='v2', context_length=512, seq_len=512, regressor=None, batch_size=256, device='auto', ignore_deps=False)[source]

Forecaster using Mantis time-series foundation model embeddings.

Mantis is primarily a time-series classification foundation model. This forecaster uses its frozen backbone as a feature extractor on rolling history windows, then fits a regression model to predict the next value. Multi-step forecasts are generated recursively.

Parameters:

checkpointstr or None, default=”paris-noah/MantisV2”: Hugging Face checkpoint to load via Mantis from_pretrained. If None, use a randomly initialized Mantis backbone.
model_version{“v1”, “v2”}, default=”v2”: Mantis architecture version. Use “v1” for “paris-noah/Mantis-8M” and “paris-noah/MantisPlus”; use “v2” for “paris-noah/MantisV2”.
context_lengthint, default=512: Number of most recent observations used for each supervised window.
seq_lenint, default=512: Length passed to Mantis. If different from context_length, windows are resized with linear interpolation.
regressorsklearn regressor or None, default=None: Regression model trained on Mantis embeddings. If None, Ridge() is used.
batch_sizeint, default=256: Batch size for Mantis embedding extraction.
devicestr, default=”auto”: Torch device. If “auto”, use CUDA when available, otherwise CPU.

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]

Feofanov et al., “Mantis: Lightweight Calibrated Foundation Model for User-Friendly Time Series Classification”, 2025. https://arxiv.org/abs/2502.15637

Examples

>>> from sktime.datasets import load_airline
>>> from sktime.forecasting.mantis import MantisForecaster
>>> y = load_airline()
>>> forecaster = MantisForecaster(context_length=24)
>>> forecaster.fit(y)
MantisForecaster(...)
>>> 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.