gluonts.torch.model.tide.estimator module#

class gluonts.torch.model.tide.estimator.TiDEEstimator(freq: str, prediction_length: int, context_length: Optional[int] = None, feat_proj_hidden_dim: Optional[int] = None, encoder_hidden_dim: Optional[int] = None, decoder_hidden_dim: Optional[int] = None, temporal_hidden_dim: Optional[int] = None, distr_hidden_dim: Optional[int] = None, num_layers_encoder: Optional[int] = None, num_layers_decoder: Optional[int] = None, decoder_output_dim: Optional[int] = None, dropout_rate: Optional[float] = None, num_feat_dynamic_proj: Optional[int] = None, num_feat_dynamic_real: int = 0, num_feat_static_real: int = 0, num_feat_static_cat: int = 0, cardinality: Optional[List[int]] = None, embedding_dimension: Optional[List[int]] = None, layer_norm: bool = False, lr: float = 0.001, weight_decay: float = 1e-08, patience: int = 10, scaling: Optional[str] = 'mean', distr_output: Output = gluonts.torch.distributions.studentT.StudentTOutput(beta=0.0), batch_size: int = 32, num_batches_per_epoch: int = 50, trainer_kwargs: Optional[Dict[str, Any]] = None, train_sampler: Optional[InstanceSampler] = None, validation_sampler: Optional[InstanceSampler] = None)[source]#

Bases: PyTorchLightningEstimator

An estimator training the TiDE model form the paper https://arxiv.org/abs/2304.08424 extended for probabilistic forecasting.

This class is uses the model defined in TiDEModel, and wraps it into a TiDELightningModule for training purposes: training is performed using PyTorch Lightning’s pl.Trainer class.

Parameters:

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

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

  • context_length – Number of time steps prior to prediction time that the model takes as inputs (default: prediction_length).

  • feat_proj_hidden_dim – Size of the feature projection layer (default: 4).

  • encoder_hidden_dim – Size of the dense encoder layer (default: 4).

  • decoder_hidden_dim – Size of the dense decoder layer (default: 4).

  • temporal_hidden_dim – Size of the temporal decoder layer (default: 4).

  • distr_hidden_dim – Size of the distribution projection layer (default: 4).

  • num_layers_encoder – Number of layers in dense encoder (default: 1).

  • num_layers_decoder – Number of layers in dense decoder (default: 1).

  • decoder_output_dim – Output size of dense decoder (default: 4).

  • dropout_rate – Dropout regularization parameter (default: 0.3).

  • num_feat_dynamic_proj – Output size of feature projection layer (default: 2).

  • num_feat_dynamic_real – Number of dynamic real features in the data (default: 0).

  • num_feat_static_real – Number of static real features in the data (default: 0).

  • num_feat_static_cat – Number of static categorical features in the data (default: 0).

  • cardinality – Number of values of each categorical feature. This must be set if num_feat_static_cat > 0 (default: None).

  • embedding_dimension – Dimension of the embeddings for categorical features (default: [16 for cat in cardinality]).

  • layer_norm – Enable layer normalization or not (default: False).

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

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

  • patience – Patience parameter for learning rate scheduler (default: 10).

  • distr_output – Distribution to use to evaluate observations and sample predictions (default: StudentTOutput()).

  • scaling – Which scaling method to use to scale the target values (default: mean).

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

  • num_batches_per_epoch – 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() → LightningModule[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: Transformation, module) → 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: Dataset, module: TiDELightningModule, 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() → 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: Dataset, module: TiDELightningModule, **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