gluonts.model.estimator module

class gluonts.model.estimator.DummyEstimator(predictor_cls: type, **kwargs)[source]

Bases: gluonts.model.estimator.Estimator

An Estimator that, upon training, simply returns a pre-constructed Predictor.

Parameters
  • predictor_clsPredictor class to instantiate.

  • **kwargs – Keyword arguments to pass to the predictor constructor.

freq = None
lead_time = None
prediction_length = None
train(training_data: Iterable[Dict[str, Any]], validation_dataset: Optional[Iterable[Dict[str, Any]]] = None) → gluonts.model.predictor.Predictor[source]

Train the estimator on the given data.

Parameters
  • training_data – Dataset to train the model on.

  • validation_data – Dataset to validate the model on during training.

Returns

The predictor containing the trained model.

Return type

Predictor

class gluonts.model.estimator.Estimator(lead_time: int = 0, **kwargs)[source]

Bases: object

An abstract class representing a trainable model.

The underlying model is trained by calling the train method with a training Dataset, producing a Predictor object.

classmethod derive_auto_fields(train_iter)[source]
freq: str = None
classmethod from_hyperparameters(**hyperparameters)[source]
classmethod from_inputs(train_iter, **params)[source]
lead_time: int = None
prediction_length: int = None
train(training_data: Iterable[Dict[str, Any]], validation_data: Optional[Iterable[Dict[str, Any]]] = None) → gluonts.model.predictor.Predictor[source]

Train the estimator on the given data.

Parameters
  • training_data – Dataset to train the model on.

  • validation_data – Dataset to validate the model on during training.

Returns

The predictor containing the trained model.

Return type

Predictor