Table Of Contents
Table Of Contents

gluonts.model.deepstate package

class gluonts.model.deepstate.DeepStateEstimator(freq: str, prediction_length: int, add_trend: bool = False, past_length: Optional[int] = None, num_periods_to_train: int = 4, trainer: gluonts.trainer._base.Trainer = gluonts.trainer._base.Trainer(batch_size=32, clip_gradient=10.0, ctx=None, epochs=25, hybridize=False, init="xavier", learning_rate=0.001, learning_rate_decay_factor=0.5, minimum_learning_rate=5e-05, num_batches_per_epoch=50, patience=10, weight_decay=1e-08), num_layers: int = 2, num_cells: int = 40, cell_type: str = 'lstm', num_eval_samples: int = 100, dropout_rate: float = 0.1, use_feat_dynamic_real: bool = False, use_feat_static_cat: bool = False, cardinality: Optional[List[int]] = None, embedding_dimension: Optional[List[int]] = None, issm: Optional[gluonts.model.deepstate.issm.ISSM] = None, scaling: bool = True, time_features: Optional[List[gluonts.time_feature._base.TimeFeature]] = None)[source]

Bases: gluonts.model.estimator.GluonEstimator

Construct a DeepState estimator.

This implements the deep state space model described in [RSG+18].

Parameters:
  • freq – Frequency of the data to train on and predict
  • prediction_length – Length of the prediction horizon
  • add_trend – Flag to indicate whether to include trend component in the state space model
  • past_length – This is the length of the training time series; i.e., number of steps to unroll the RNN for before computing predictions. Set this to (at most) the length of the shortest time series in the dataset. (default: None, in which case the training length is set such that at least num_seasons_to_train seasons are included in the training. See num_seasons_to_train)
  • num_periods_to_train – (Used only when past_length is not set) Number of periods to include in the training time series. (default: 4) Here period corresponds to the longest cycle one can expect given the granularity of the time series. See: https://stats.stackexchange.com/questions/120806/frequency-value-for-seconds-minutes-intervals-data-in-r
  • trainer – Trainer object to be used (default: Trainer())
  • num_layers – Number of RNN layers (default: 2)
  • num_cells – Number of RNN cells for each layer (default: 40)
  • cell_type – Type of recurrent cells to use (available: ‘lstm’ or ‘gru’; default: ‘lstm’)
  • num_eval_samples – Number of samples paths to draw when computing predictions (default: 100)
  • dropout_rate – Dropout regularization parameter (default: 0.1)
  • use_feat_dynamic_real – Whether to use the feat_dynamic_real field from the data (default: False)
  • use_feat_static_cat – Whether to use the feat_static_cat field from the data (default: False)
  • cardinality – Number of values of each categorical feature. This must be set if use_feat_static_cat == True (default: None)
  • embedding_dimension – Dimension of the embeddings for categorical features (default: [min(50, (cat+1)//2) for cat in cardinality])
  • scaling – Whether to automatically scale the target values (default: true)
  • time_features – Time features to use as inputs of the RNN (default: None, in which case these are automatically determined based on freq)
create_predictor(transformation: gluonts.transform.Transformation, trained_network: mxnet.gluon.block.HybridBlock) → 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_network() → gluonts.model.deepstate._network.DeepStateTrainingNetwork[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.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