gluonts.mx.model.lstnet package#

class gluonts.mx.model.lstnet.LSTNetEstimator(prediction_length: int, context_length: int, num_series: int, skip_size: int, ar_window: int, channels: int, lead_time: int = 0, kernel_size: int = 6, trainer: gluonts.mx.trainer._base.Trainer = gluonts.mx.trainer._base.Trainer(add_default_callbacks=True, callbacks=None, clip_gradient=10.0, ctx=None, epochs=100, hybridize=True, init='xavier', learning_rate=0.001, num_batches_per_epoch=50, weight_decay=1e-08), dropout_rate: typing.Optional[float] = 0.2, output_activation: typing.Optional[str] = None, rnn_cell_type: str = 'gru', rnn_num_cells: int = 100, rnn_num_layers: int = 3, skip_rnn_cell_type: str = 'gru', skip_rnn_num_layers: int = 1, skip_rnn_num_cells: int = 10, scaling: bool = True, train_sampler: typing.Optional[gluonts.transform.sampler.InstanceSampler] = None, validation_sampler: typing.Optional[gluonts.transform.sampler.InstanceSampler] = None, batch_size: int = 32, dtype: typing.Type = <class 'numpy.float32'>)[source]#

Bases: gluonts.mx.model.estimator.GluonEstimator

Constructs an LSTNet estimator for multivariate time series data.

The model has been described in this paper: https://arxiv.org/abs/1703.07015

Note that this implementation will change over time as we further work on this method.

Parameters

  • prediction_length (int) – Length of the prediction p where given (y_1, …, y_t) the model predicts (y_{t+l+1}, …, y_{t+l+p}), where l is lead_time

  • context_length – The maximum number of steps to unroll the RNN for computing the predictions (Note that it is constraints by the Conv2D output size)

  • num_series – Number of time series (covariates)

  • skip_size – Skip size for the skip RNN layer

  • ar_window – Auto-regressive window size for the linear part

  • channels – Number of channels for first layer Conv2D

  • lead_time (int) – Lead time (default: 0)

  • kernel_size – Kernel size for first layer Conv2D (default: 6)

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

  • dropout_rate – Dropout regularization parameter (default: 0.2)

  • output_activation – The last activation to be used for output. Accepts either None (default no activation), sigmoid or tanh

  • rnn_cell_type – Type of the RNN cell. Either lstm or gru (default: gru)

  • rnn_num_layers – Number of RNN layers to be used

  • rnn_num_cells – Number of RNN cells for each layer (default: 100)

  • skip_rnn_cell_type – Type of the RNN cell for the skip layer. Either lstm or gru ( default: gru)

  • skip_rnn_num_layers – Number of RNN layers to be used for skip part

  • skip_rnn_num_cells – Number of RNN cells for each layer for skip part (default: 10)

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

  • train_sampler – Controls the sampling of windows during training.

  • validation_sampler – Controls the sampling of windows during validation.

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

  • dtype – Data type (default: np.float32)

create_predictor(transformation: gluonts.transform._base.Transformation, trained_network: mxnet.gluon.block.HybridBlock) → gluonts.model.predictor.Predictor[source]#

Create and return a predictor object.

Parameters

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

  • module – A trained HybridBlock object.

Returns

A predictor wrapping a HybridBlock used for inference.

Return type

Predictor

create_training_data_loader(data: gluonts.dataset.Dataset, **kwargs) → Iterable[Dict[str, Any]][source]#

Create a data loader for training purposes.

Parameters

data – Dataset from which to create the data loader.

Returns

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

Return type

DataLoader

create_training_network() → mxnet.gluon.block.HybridBlock[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: gluonts.dataset.Dataset, **kwargs) → Iterable[Dict[str, Any]][source]#

Create a data loader for validation purposes.

Parameters

data – Dataset from which to create the data loader.

Returns

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

Return type

DataLoader

lead_time: int#
prediction_length: int#