# 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]

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. 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. 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. Transformation