gluonts.model.tft package

class gluonts.model.tft.TemporalFusionTransformerEstimator(freq: str, prediction_length: int, context_length: Optional[int] = None, trainer: =, batch_size=None, callbacks=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, weight_decay=1e-08), hidden_dim: int = 32, variable_dim: Optional[int] = None, num_heads: int = 4, num_outputs: int = 3, num_instance_per_series: int = 100, dropout_rate: float = 0.1, time_features: List[gluonts.time_feature._base.TimeFeature] = [], static_cardinalities: Dict[str, int] = {}, dynamic_cardinalities: Dict[str, int] = {}, static_feature_dims: Dict[str, int] = {}, dynamic_feature_dims: Dict[str, int] = {}, past_dynamic_features: List[str] = [], 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: gluonts.dataset.common.Dataset, **kwargs) → Iterable[Dict[str, Any]][source]
create_training_network() → gluonts.model.tft._network.TemporalFusionTransformerTrainingNetwork[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: gluonts.dataset.common.Dataset, **kwargs) → Iterable[Dict[str, Any]][source]
freq = None
lead_time = None
prediction_length = None