TafsutForecaster

class TafsutForecaster(model_path: str | None = 'Tafsut-FM/tafsut-univariate-base', config: dict | None = None, device=None)[source]

Zero-shot probabilistic forecaster using the Tafsut foundation model.

Tafsut is a univariate, probabilistic time-series foundation model. It returns forecasts at nine quantile levels from 0.1 through 0.9 and does not support exogenous variables or in-sample predictions.

Parameters:

model_pathstr or None, default=”Tafsut-FM/tafsut-univariate-base”: Local model directory or Hugging Face model repository identifier. If None, initialize a random model from config.
configdict, optional: Configuration values for a model initialized with model_path=None.
devicestr or torch.device, optional: Device used for inference. If None, Tafsut selects CUDA when available and CPU otherwise.

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.

Examples

>>> import pandas as pd
>>> from sktime.forecasting.tafsut import TafsutForecaster
>>> config = {
...     "context_length": 8,
...     "prediction_length": 4,
...     "input_patch_size": 2,
...     "output_patch_size": 2,
...     "input_patch_stride": 2,
...     "d_model": 8,
...     "d_kv": 2,
...     "d_ff": 16,
...     "num_layers": 1,
...     "num_heads": 4,
...     "dropout_rate": 0.0,
... }
>>> forecaster = TafsutForecaster(
...     model_path=None, config=config, device="cpu"
... )
>>> y = pd.Series(range(8))
>>> _ = forecaster.fit(y, fh=[1, 2, 3, 4])
>>> y_pred = forecaster.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 parameter settings for estimator testing.
`is_composite`()	Check if the object is composed of other BaseObjects.
`load`()	Load and cache the underlying Tafsut model.
`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.