gluonts.torch.model.deepar.estimator module

class gluonts.torch.model.deepar.estimator.DeepAREstimator(freq: str, prediction_length: int, context_length: Optional[int] = None, num_layers: int = 2, hidden_size: int = 40, dropout_rate: float = 0.1, num_feat_dynamic_real: int = 0, num_feat_static_cat: int = 0, num_feat_static_real: int = 0, cardinality: Optional[List[int]] = None, embedding_dimension: Optional[List[int]] = None, distr_output: gluonts.torch.modules.distribution_output.DistributionOutput = gluonts.torch.modules.distribution_output.StudentTOutput(), loss: gluonts.torch.modules.loss.DistributionLoss = NegativeLogLikelihood(), scaling: bool = True, lags_seq: Optional[List[int]] = None, time_features: Optional[List[gluonts.time_feature._base.TimeFeature]] = None, num_parallel_samples: int = 100, batch_size: int = 32, num_batches_per_epoch: int = 50, trainer_kwargs: Optional[Dict[str, Any]] = {})[source]

Bases: gluonts.torch.model.estimator.PyTorchLightningEstimator

create_lightning_module() → gluonts.torch.model.deepar.lightning_module.DeepARLightningModule[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

nn.Module

create_predictor(transformation: gluonts.transform._base.Transformation, module: gluonts.torch.model.deepar.lightning_module.DeepARLightningModule) → gluonts.torch.model.predictor.PyTorchPredictor[source]

Create and return a predictor object.

Returns

A predictor wrapping a nn.Module used for inference.

Return type

Predictor

create_training_data_loader(data: gluonts.dataset.common.Dataset, module: gluonts.torch.model.deepar.lightning_module.DeepARLightningModule, shuffle_buffer_length: Optional[int] = None, **kwargs) → Iterable[source]
create_transformation() → gluonts.transform._base.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

create_validation_data_loader(data: gluonts.dataset.common.Dataset, module: gluonts.torch.model.deepar.lightning_module.DeepARLightningModule, **kwargs) → Iterable[source]
freq = None
lead_time = None
prediction_length = None