Table Of Contents
Table Of Contents

gluonts.model.deep_factor package

class gluonts.model.deep_factor.DeepFactorEstimator(freq: str, context_length: int, prediction_length: int, num_layers: int = 1, num_hidden: int = 50, num_factor: int = 10, num_hidden_noise: int = 5, num_layers_noise: int = 1, cell_type: str = 'lstm', trainer: gluonts.trainer._base.Trainer = gluonts.trainer._base.Trainer(batch_size=32, clip_gradient=10.0, ctx=None, epochs=100, hybridize=True, init="xavier", learning_rate=0.001, learning_rate_decay_factor=0.5, minimum_learning_rate=5e-05, num_batches_per_epoch=50, patience=10, weight_decay=1e-08), num_eval_samples: int = 100, cardinality: List[int] = [1], embedding_dimension: int = 10, distr_output: gluonts.distribution.distribution_output.DistributionOutput = gluonts.distribution.student_t.StudentTOutput())[source]

Bases: gluonts.model.estimator.GluonEstimator

create_predictor(transformation: gluonts.transform.Transformation, trained_network: gluonts.model.deep_factor._network.DeepFactorTrainingNetwork) → gluonts.model.predictor.Predictor[source]

Create and return a predictor object.

Returns:A predictor wrapping a HybridBlock used for inference.
Return type:Predictor
create_training_network() → gluonts.model.deep_factor._network.DeepFactorTrainingNetwork[source]

Create and return the network used for training (i.e., computing the loss).

Returns:The network that computes the loss given input data.
Return type:HybridBlock
create_transformation() → gluonts.transform.Transformation[source]

Create and return the transformation needed for training and inference.

Returns:The transformation that will be applied entry-wise to datasets, at training and inference time.
Return type:Transformation