gluonts.model.canonical package¶
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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())[source]¶ Bases:
gluonts.model.canonical._estimator.CanonicalEstimator
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freq
= None¶
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lead_time
= None¶
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prediction_length
= None¶
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