gluonts.torch.model.d_linear package#

class gluonts.torch.model.d_linear.DLinearEstimator(prediction_length: int, context_length: Optional[int] = None, hidden_dimension: Optional[int] = None, lr: float = 0.001, weight_decay: float = 1e-08, scaling: Optional[str] = 'mean', distr_output: gluonts.torch.distributions.output.Output = gluonts.torch.distributions.studentT.StudentTOutput(beta=0.0), kernel_size: int = 25, 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

An estimator training the d-linear model form the paper https://arxiv.org/pdf/2205.13504.pdf extended for probabilistic forecasting.

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

Parameters

  • 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: 10 * prediction_length).

  • hidden_dimension – Size of representation.

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

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

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

  • kernel_size –

  • 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() → lightning.pytorch.core.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: gluonts.transform._base.Transformation, module) → 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.d_linear.lightning_module.DLinearLightningModule, 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.d_linear.lightning_module.DLinearLightningModule, **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

lead_time: int#
prediction_length: int#
class gluonts.torch.model.d_linear.DLinearLightningModule(model_kwargs: dict, lr: float = 0.001, weight_decay: float = 1e-08)[source]#

Bases: lightning.pytorch.core.module.LightningModule

A pl.LightningModule class that can be used to train a DLinearModel with PyTorch Lightning.

This is a thin layer around a (wrapped) DLinearModel object, that exposes the methods to evaluate training and validation loss.

Parameters

  • model_kwargs – Keyword arguments to construct the DLinearModel to be trained.

  • loss – Loss function to be used for training.

  • lr – Learning rate.

  • weight_decay – Weight decay regularization parameter.

configure_optimizers()[source]#

Returns the optimizer to use.

forward(*args, **kwargs)[source]#

Same as torch.nn.Module.forward().

Parameters

  • *args – Whatever you decide to pass into the forward method.

  • **kwargs – Keyword arguments are also possible.

Returns

Your model’s output

training_step(batch, batch_idx: int)[source]#

Execute training step.

validation_step(batch, batch_idx: int)[source]#

Execute validation step.

class gluonts.torch.model.d_linear.DLinearModel(prediction_length: int, context_length: int, hidden_dimension: int, distr_output=gluonts.torch.distributions.studentT.StudentTOutput(beta=0.0), kernel_size: int = 25, scaling: str = 'mean')[source]#

Bases: torch.nn.modules.module.Module

Module implementing a feed-forward model form the paper https://arxiv.org/pdf/2205.13504.pdf extended for probabilistic forecasting.

Parameters

  • prediction_length – Number of time points to predict.

  • context_length – Number of time steps prior to prediction time that the model.

  • hidden_dimension – Size of last hidden layers in the feed-forward network.

  • distr_output – Distribution to use to evaluate observations and sample predictions.

describe_inputs(batch_size=1) → gluonts.model.inputs.InputSpec[source]#
forward(past_target: torch.Tensor, past_observed_values: torch.Tensor) → Tuple[Tuple[torch.Tensor, ...], torch.Tensor, torch.Tensor][source]#

Define the computation performed at every call.

Should be overridden by all subclasses.

Note

Although the recipe for forward pass needs to be defined within this function, one should call the Module instance afterwards instead of this since the former takes care of running the registered hooks while the latter silently ignores them.

loss(past_target: torch.Tensor, past_observed_values: torch.Tensor, future_target: torch.Tensor, future_observed_values: torch.Tensor) → torch.Tensor[source]#
training: bool#