PatchTSMixerForecaster

class PatchTSMixerForecaster(model_path: str | None = 'ibm-granite/granite-timeseries-patchtsmixer', revision: str = 'main', config: dict | None = None, context_length: int | None = None, prediction_length: int | None = None, validation_split: float = 0.2, train_model: bool = True, scaling: bool = True, training_args: dict | None = None, callbacks: list | None = None, num_parallel_samples: int | None = None)[source]

Forecaster wrapping IBM PatchTSMixer (granite-tsfm / Hugging Face).

PatchTSMixer, developed by IBM, is a Lightweight MLP-Mixer Model for Multivariate Time Series Forecasting. Implementation inspired by [1].

y should use a DatetimeIndex (or PeriodIndex). Endogenous columns in y are forecast jointly; there is no exogenous X support. If y has no time index, index is reset, and a synthetic daily timestamp column is used instead.

Parameters:

model_pathstr, optional, default=”ibm-granite/granite-timeseries-patchtsmixer”: Hugging Face model id or local checkpoint path. If None, the model is initialized from config only (train from scratch).
revisionstr, default=”main”: Hub revision for from_pretrained.
configdict, optional, default=None: Extra fields for PatchTSMixerConfig (e.g. d_model, patch_length).
context_lengthint, optional, default=None: Input history length for sliding windows. If None, taken from the loaded config or defaults to 512 when training from scratch.
prediction_lengthint, optional, default=None: Forecast horizon length for the model head. If None, uses max(fh) when fh is passed to fit, else the loaded config default.
validation_splitfloat, optional, default=0.2: Fraction of y held out for validation during Trainer training.
train_modelbool, default=True: If True, run Trainer.train() on y. If False, only fit the preprocessor and load weights (pretrained model evaluate path).
scalingbool, default=True: Whether TimeSeriesPreprocessor standardizes targets.
training_argsdict, optional, default=None: Passed to TrainingArguments (label_names=["future_values"] is set if missing).
callbackslist, optional, default=None: Hugging Face Trainer callbacks (e.g. EarlyStoppingCallback).
num_parallel_samplesint, optional, default=None: Override num_parallel_samples on the model for generate.

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://github.com/ibm-granite/granite-tsfm/blob/main/notebooks/hfdemo/patch_tsmixer_getting_started.ipynb

[2]

Ekambaram et al., TSMixer: Lightweight MLP-Mixer Model for Multivariate Time Series Forecasting, arXiv:2306.09364

Examples

>>> from sktime.datasets import load_airline
>>> from sktime.forecasting.patch_tsmixer import PatchTSMixerForecaster
>>> from sktime.split import temporal_train_test_split
>>> y = load_airline()
>>> y_train, _ = temporal_train_test_split(y)
>>> f = PatchTSMixerForecaster(
...     model_path=None,
...     config={
...         "context_length": 8,
...         "prediction_length": 3,
...         "patch_length": 2,
...         "patch_stride": 2,
...         "num_input_channels": 1,
...         "d_model": 16,
...         "num_layers": 1,
...     },
...     training_args={
...         "output_dir": "test_output",
...         "max_steps": 2,
...         "per_device_train_batch_size": 4,
...         "report_to": "none",
...     },
... )
>>> f.fit(y_train, fh=[1, 2, 3])
>>> y_pred = f.predict()

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.