gluonts.model.canonical package

class gluonts.model.canonical.CanonicalRNNEstimator(freq: str, context_length: int, prediction_length: int, 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), num_layers: int = 1, num_cells: int = 50, cell_type: str = 'lstm', 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())

Bases: gluonts.model.canonical._estimator.CanonicalEstimator

freq = None

lead_time = None

prediction_length = None