gluonts.torch.distributions.quantile_output module#

class gluonts.torch.distributions.quantile_output.QuantileOutput(quantiles: List[float])[source]#

Bases: Output

domain_map(*args: Tensor) → Tuple[Tensor, ...][source]#

Converts arguments to the right shape and domain.

The domain depends on the type of distribution, while the correct shape is obtained by reshaping the trailing axis in such a way that the returned tensors define a distribution of the right event_shape.

property event_shape: Tuple#

Shape of each individual event compatible with the output object.

property forecast_generator: ForecastGenerator#
loss(target: Tensor, distr_args: Tuple[Tensor, ...], loc: Optional[Tensor] = None, scale: Optional[Tensor] = None) → Tensor[source]#

Compute loss for target data given network output.

Parameters:

  • target – Values of the target time series for which loss is to be computed.

  • distr_args – Arguments that can be used to construct the output distribution.

  • loc – Location parameter of the distribution, optional.

  • scale – Scale parameter of the distribution, optional.

Returns:

Values of the loss, has same shape as target.

Return type:

loss_values

property quantiles: List[float]#