gluonts.model.tft package#
- class gluonts.model.tft.TemporalFusionTransformerEstimator(freq: str, prediction_length: int, context_length: Optional[int] = None, trainer: gluonts.mx.trainer._base.Trainer = gluonts.mx.trainer._base.Trainer(add_default_callbacks=True, 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]#
Bases:
gluonts.mx.model.estimator.GluonEstimator- create_predictor(transformation: gluonts.transform._base.Transformation, trained_network: mxnet.gluon.block.HybridBlock) gluonts.mx.model.predictor.RepresentableBlockPredictor[source]#
Create and return a predictor object.
- Parameters
transformation – Transformation to be applied to data before it goes into the model.
module – A trained HybridBlock object.
- Returns
A predictor wrapping a HybridBlock used for inference.
- Return type
- create_training_data_loader(data: gluonts.dataset.Dataset, **kwargs) Iterable[Dict[str, Any]][source]#
Create a data loader for training purposes.
- Parameters
data – Dataset from which to create the data loader.
- Returns
The data loader, i.e. and iterable over batches of data.
- Return type
DataLoader
- create_training_network() gluonts.model.tft._network.TemporalFusionTransformerTrainingNetwork[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._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
- create_validation_data_loader(data: gluonts.dataset.Dataset, **kwargs) Iterable[Dict[str, Any]][source]#
Create a data loader for validation purposes.
- Parameters
data – Dataset from which to create the data loader.
- Returns
The data loader, i.e. and iterable over batches of data.
- Return type
DataLoader
- lead_time: int#
- prediction_length: int#