Table Of Contents
Table Of Contents

gluonts.model.simple_feedforward package

class gluonts.model.simple_feedforward.SimpleFeedForwardEstimator(freq: str, prediction_length: int, trainer: gluonts.trainer._base.Trainer = gluonts.trainer._base.Trainer(batch_size=32, 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, weight_decay=1e-08), num_hidden_dimensions: List[int] = [40, 40], context_length: Optional[int] = None, distr_output: gluonts.distribution.distribution_output.DistributionOutput = gluonts.distribution.student_t.StudentTOutput(), batch_normalization: bool = False, mean_scaling: bool = True, num_parallel_samples: int = 100)[source]

Bases: gluonts.model.estimator.GluonEstimator

SimpleFeedForwardEstimator shows how to build a simple MLP model predicting the next target time-steps given the previous ones.

Given that we want to define a gluon model trainable by SGD, we inherit the parent class GluonEstimator that handles most of the logic for fitting a neural-network.

We thus only have to define:

  1. How the data is transformed before being fed to our model:

    def create_transformation(self) -> Transformation
    
  2. How the training happens:

    def create_training_network(self) -> HybridBlock
    
  3. how the predictions can be made for a batch given a trained network:

    def create_predictor(
         self,
         transformation: Transformation,
         trained_net: HybridBlock,
    ) -> Predictor
    
Parameters:
  • freq – Time time granularity of the data
  • prediction_length – Length of the prediction horizon
  • trainer – Trainer object to be used (default: Trainer())
  • num_hidden_dimensions – Number of hidden nodes in each layer (default: [40, 40])
  • context_length – Number of time units that condition the predictions (default: None, in which case context_length = prediction_length)
  • distr_output – Distribution to fit (default: StudentTOutput())
  • batch_normalization – Whether to use batch normalization (default: False)
  • mean_scaling – Scale the network input by the data mean and the network output by its inverse (default: True)
  • 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)
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() → 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.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