gluonts.torch.model.tft.estimator module#

class gluonts.torch.model.tft.estimator.TemporalFusionTransformerEstimator(freq: str, prediction_length: int, context_length: Optional[int] = None, quantiles: Optional[List[float]] = None, distr_output: Optional[gluonts.torch.distributions.output.Output] = None, num_heads: int = 4, hidden_dim: int = 32, variable_dim: int = 32, static_dims: Optional[List[int]] = None, dynamic_dims: Optional[List[int]] = None, past_dynamic_dims: Optional[List[int]] = None, static_cardinalities: Optional[List[int]] = None, dynamic_cardinalities: Optional[List[int]] = None, past_dynamic_cardinalities: Optional[List[int]] = None, time_features: Optional[List[Callable[[pandas.core.indexes.period.PeriodIndex], numpy.ndarray]]] = None, lr: float = 0.001, weight_decay: float = 1e-08, dropout_rate: float = 0.1, patience: int = 10, batch_size: int = 32, num_batches_per_epoch: int = 50, trainer_kwargs: Optional[Dict[str, Any]] = None, train_sampler: Optional[gluonts.transform.sampler.InstanceSampler] = None, validation_sampler: Optional[gluonts.transform.sampler.InstanceSampler] = None)[source]#

Bases: gluonts.torch.model.estimator.PyTorchLightningEstimator

Estimator class to train a Temporal Fusion Transformer (TFT) model, as described in [LAL+21].

TFT internally performs feature selection when making forecasts. For this reason, the dimensions of real-valued features can be grouped together if they correspond to the same variable (e.g., treat weather features as a one feature and holiday indicators as another feature).

For example, if the dataset contains key “feat_static_real” with shape [batch_size, 3], we can, e.g., - set static_dims = [3] to treat all three dimensions as a single feature - set static_dims = [1, 1, 1] to treat each dimension as a separate feature - set static_dims = [2, 1] to treat the first two dims as a single feature

See gluonts.torch.model.tft.TemporalFusionTransformerModel.input_shapes for more details on how the model configuration corresponds to the expected input shapes.

Parameters

  • freq – Frequency of the data to train on and predict.

  • prediction_length (int) – Length of the prediction horizon.

  • context_length – Number of previous time series values provided as input to the encoder. (default: None, in which case context_length = prediction_length).

  • quantiles – List of quantiles that the model will learn to predict. Defaults to [0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9]

  • distr_output – Distribution output to use (default: QuantileOutput).

  • num_heads – Number of attention heads in self-attention layer in the decoder.

  • hidden_dim – Size of the LSTM & transformer hidden states.

  • variable_dim – Size of the feature embeddings.

  • static_dims – Sizes of the real-valued static features.

  • dynamic_dims – Sizes of the real-valued dynamic features that are known in the future.

  • past_dynamic_dims – Sizes of the real-valued dynamic features that are only known in the past.

  • static_cardinalities – Cardinalities of the categorical static features.

  • dynamic_cardinalities – Cardinalities of the categorical dynamic features that are known in the future.

  • past_dynamic_cardinalities – Cardinalities of the categorical dynamic features that are ony known in the past.

  • time_features – List of time features, from gluonts.time_feature, to use as dynamic real features in addition to the provided data (default: None, in which case these are automatically determined based on freq).

  • lr – Learning rate (default: 1e-3).

  • weight_decay – Weight decay (default: 1e-8).

  • dropout_rate – Dropout regularization parameter (default: 0.1).

  • patience – Patience parameter for learning rate scheduler.

  • batch_size – The size of the batches to be used for training (default: 32).

  • num_batches_per_epoch (int = 50,) – Number of batches to be processed in each training epoch (default: 50).

  • trainer_kwargs – Additional arguments to provide to pl.Trainer for construction.

  • train_sampler – Controls the sampling of windows during training.

  • validation_sampler – Controls the sampling of windows during validation.

create_lightning_module() → gluonts.torch.model.tft.lightning_module.TemporalFusionTransformerLightningModule[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

pl.LightningModule

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

Create and return a predictor object.

Parameters

  • transformation – Transformation to be applied to data before it goes into the model.

  • module – A trained pl.LightningModule object.

Returns

A predictor wrapping a nn.Module used for inference.

Return type

Predictor

create_training_data_loader(data: gluonts.dataset.Dataset, module: gluonts.torch.model.tft.lightning_module.TemporalFusionTransformerLightningModule, shuffle_buffer_length: Optional[int] = None, **kwargs) → Iterable[source]#

Create a data loader for training purposes.

Parameters

  • data – Dataset from which to create the data loader.

  • module – The pl.LightningModule object that will receive the batches from the data loader.

Returns

The data loader, i.e. and iterable over batches of data.

Return type

Iterable

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.Dataset, module: gluonts.torch.model.tft.lightning_module.TemporalFusionTransformerLightningModule, **kwargs) → Iterable[source]#

Create a data loader for validation purposes.

Parameters

  • data – Dataset from which to create the data loader.

  • module – The pl.LightningModule object that will receive the batches from the data loader.

Returns

The data loader, i.e. and iterable over batches of data.

Return type

Iterable

input_names()[source]#
lead_time: int#
prediction_length: int#