gluonts.model.estimator module

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

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)

classmethod from_hyperparameters(**hyperparameters)

classmethod from_inputs(train_iter, **params)

lead_time: int

prediction_length: int

train(training_data: Dataset, validation_data: Optional[Dataset] = None) → Predictor

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.IncrementallyTrainable(*args, **kwargs)

Bases: Protocol

train_from(predictor: Predictor, training_data: Dataset, validation_data: Optional[Dataset] = None) → Predictor

Experimental: this feature may change in future versions. Train the estimator, starting from a previously trained predictor, on the given data.

Parameters:

• predictor – A previously trained model, from which to initialize the estimator training.

• 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