gluonts.model.deep_factor package

class gluonts.model.deep_factor.DeepFactorEstimator(freq: str, prediction_length: int, num_hidden_global: int = 50, num_layers_global: int = 1, num_factors: int = 10, num_hidden_local: int = 5, num_layers_local: int = 1, cell_type: str = 'lstm', trainer: gluonts.mx.trainer._base.Trainer = gluonts.mx.trainer._base.Trainer(avg_strategy=gluonts.mx.trainer.model_averaging.SelectNBestMean(maximize=False, metric="score", num_models=1), batch_size=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, post_initialize_cb=None, weight_decay=1e-08), context_length: Optional[int] = None, num_parallel_samples: int = 100, cardinality: List[int] = [1], embedding_dimension: int = 10, distr_output: gluonts.mx.distribution.distribution_output.DistributionOutput = gluonts.mx.distribution.student_t.StudentTOutput(), batch_size: int = 32)[source]

Bases: gluonts.mx.model.estimator.GluonEstimator

DeepFactorEstimator is an implementation of the 2019 ICML paper “Deep Factors for Forecasting” https://arxiv.org/abs/1905.12417. It uses a global RNN model to learn patterns across multiple related time series and an arbitrary local model to model the time series on a per time series basis. In the current implementation, the local model is a RNN (DF-RNN).

Parameters

  • freq – Time series frequency.

  • prediction_length – Prediction length.

  • num_hidden_global – Number of units per hidden layer for the global RNN model (default: 50).

  • num_layers_global – Number of hidden layers for the global RNN model (default: 1).

  • num_factors – Number of global factors (default: 10).

  • num_hidden_local – Number of units per hidden layer for the local RNN model (default: 5).

  • num_layers_local – Number of hidden layers for the global local model (default: 1).

  • cell_type – Type of recurrent cells to use (available: ‘lstm’ or ‘gru’; default: ‘lstm’).

  • trainer – Trainer object to be used (default: Trainer()).

  • context_length – Training length (default: None, in which case context_length = prediction_length).

  • num_parallel_samples – Number of evaluation samples per time series to increase parallelism during inference. This is a model optimization that does not affect the accuracy (default: 100).

  • cardinality – List consisting of the number of time series (default: list([1]).

  • embedding_dimension – Dimension of the embeddings for categorical features (the same dimension is used for all embeddings, default: 10).

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

  • batch_size – The size of the batches to be used training and prediction.

create_predictor(transformation: gluonts.transform._base.Transformation, trained_network: gluonts.model.deep_factor._network.DeepFactorTrainingNetwork) → gluonts.model.predictor.Predictor[source]

Create and return a predictor object.

Returns

A predictor wrapping a HybridBlock used for inference.

Return type

Predictor

create_training_data_loader(data: Iterable[Dict[str, Any]], **kwargs) → gluonts.dataset.loader.DataLoader[source]
create_training_network() → gluonts.model.deep_factor._network.DeepFactorTrainingNetwork[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

Transformation

create_validation_data_loader(data: Iterable[Dict[str, Any]], **kwargs) → gluonts.dataset.loader.DataLoader[source]
freq = None
lead_time = None
prediction_length = None