# gluonts.model.deepstate package¶

class gluonts.model.deepstate.DeepStateEstimator(freq: str, prediction_length: int, cardinality: List[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=100, 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_parallel_samples: int = 100, dropout_rate: float = 0.1, use_feat_dynamic_real: bool = False, use_feat_static_cat: bool = True, 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, noise_std_bounds: gluonts.distribution.lds.ParameterBounds = gluonts.distribution.lds.ParameterBounds(lower=1e-06, upper=1.0), prior_cov_bounds: gluonts.distribution.lds.ParameterBounds = gluonts.distribution.lds.ParameterBounds(lower=1e-06, upper=1.0), innovation_bounds: gluonts.distribution.lds.ParameterBounds = gluonts.distribution.lds.ParameterBounds(lower=1e-06, upper=0.01))[source]

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

• cardinality – Number of values of each categorical feature. This must be set by default unless use_feat_static_cat is set to False explicitly (which is NOT recommended).

• 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_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).

• 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: True)

• 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)

• noise_std_bounds – Lower and upper bounds for the standard deviation of the observation noise

• prior_cov_bounds – Lower and upper bounds for the diagonal of the prior covariance matrix

• innovation_bounds – Lower and upper bounds for the standard deviation of the observation noise

create_predictor(transformation: gluonts.transform._base.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._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

freq = None
lead_time = None
prediction_length = None