gluonts.model.san package

class gluonts.model.san.SelfAttentionEstimator(freq: str, prediction_length: int, cardinalities: Optional[List[int]] = None, context_length: Optional[int] = None, trainer: =, metric="score", num_models=1), batch_size=None, 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, post_initialize_cb=None, weight_decay=1e-08), model_dim: int = 64, ffn_dim_multiplier: int = 2, num_heads: int = 4, num_layers: int = 3, num_outputs: int = 3, kernel_sizes: List[int] = [3, 5, 7, 9], distance_encoding: Optional[str] = 'dot', pre_layer_norm: bool = False, dropout: float = 0.1, temperature: float = 1.0, time_features: Optional[List[gluonts.time_feature._base.TimeFeature]] = None, use_feat_dynamic_real: bool = True, use_feat_dynamic_cat: bool = False, use_feat_static_real: bool = False, use_feat_static_cat: bool = True, train_sampler: Optional[gluonts.transform.sampler.InstanceSampler] = None, validation_sampler: Optional[gluonts.transform.sampler.InstanceSampler] = None, batch_size: int = 32)[source]


create_predictor(transformation: gluonts.transform._base.Transformation, trained_network: mxnet.gluon.block.HybridBlock) →[source]

Create and return a predictor object.


A predictor wrapping a HybridBlock used for inference.

Return type


create_training_data_loader(data: Iterable[Dict[str, Any]], **kwargs) → gluonts.dataset.loader.DataLoader[source]
create_training_network() → gluonts.model.san._network.SelfAttentionTrainingNetwork[source]

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


The network that computes the loss given input data.

Return type


create_transformation() → gluonts.transform._base.Transformation[source]

Create and return the transformation needed for training and inference.


The transformation that will be applied entry-wise to datasets, at training and inference time.

Return type


create_validation_data_loader(data: Iterable[Dict[str, Any]], **kwargs) → gluonts.dataset.loader.DataLoader[source]
freq = None
lead_time = None
prediction_length = None