gluonts.torch.model.deepar.estimator module

class gluonts.torch.model.deepar.estimator.DeepAREstimator(freq: str, prediction_length: int, context_length: Optional[int] = None, num_layers: int = 2, hidden_size: int = 40, lr: float = 0.001, weight_decay: float = 1e-08, dropout_rate: float = 0.1, patience: int = 10, num_feat_dynamic_real: int = 0, num_feat_static_cat: int = 0, num_feat_static_real: int = 0, cardinality: Optional[List[int]] = None, embedding_dimension: Optional[List[int]] = None, distr_output: gluonts.torch.distributions.distribution_output.DistributionOutput = gluonts.torch.distributions.studentT.StudentTOutput(beta=0.0), scaling: bool = True, default_scale: Optional[float] = None, lags_seq: Optional[List[int]] = None, time_features: Optional[List[Callable[[pandas.core.indexes.period.PeriodIndex], numpy.ndarray]]] = None, num_parallel_samples: int = 100, batch_size: int = 32, num_batches_per_epoch: int = 50, imputation_method: Optional[gluonts.transform.feature.MissingValueImputation] = None, trainer_kwargs: Optional[Dict[str, Any]] = None, train_sampler: Optional[gluonts.transform.sampler.InstanceSampler] = None, validation_sampler: Optional[gluonts.transform.sampler.InstanceSampler] = None, nonnegative_pred_samples: bool = False)

Bases: gluonts.torch.model.estimator.PyTorchLightningEstimator

Estimator class to train a DeepAR model, as described in [SFG17].

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

Note: the code of this model is unrelated to the implementation behind SageMaker’s DeepAR Forecasting Algorithm.

Parameters

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

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

• context_length – Number of steps to unroll the RNN for before computing predictions (default: None, in which case context_length = prediction_length).

• num_layers – Number of RNN layers (default: 2).

• hidden_size – Number of RNN cells for each layer (default: 40).

• lr – Learning rate (default: 1e-3).

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

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

• patience – Patience parameter for learning rate scheduler.

• 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: [min(50, (cat+1)//2) for cat in cardinality]).

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

• scaling – Whether to automatically scale the target values (default: true).

• default_scale – Default scale that is applied if the context length window is completely unobserved. If not set, the scale in this case will be the mean scale in the batch.

• lags_seq – Indices of the lagged target values to use as inputs of the RNN (default: None, in which case these are automatically determined based on freq).

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

• num_parallel_samples – Number of samples per time series to that the resulting predictor should produce (default: 100).

• 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.

• nonnegative_pred_samples – Should final prediction samples be non-negative? If yes, an activation function is applied to ensure non-negative. Observe that this is applied only to the final samples and this is not applied during training.

create_lightning_module() → gluonts.torch.model.deepar.lightning_module.DeepARLightningModule

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.deepar.lightning_module.DeepARLightningModule) → gluonts.torch.model.predictor.PyTorchPredictor

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.deepar.lightning_module.DeepARLightningModule, shuffle_buffer_length: Optional[int] = None, **kwargs) → Iterable

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

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.deepar.lightning_module.DeepARLightningModule, **kwargs) → Iterable

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

classmethod derive_auto_fields(train_iter)

lead_time: int

prediction_length: int