gluonts.torch.distributions.quantile_output module

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

Bases: gluonts.torch.distributions.output.Output

args_dim: Dict[str, int]

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

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: gluonts.model.forecast_generator.ForecastGenerator

in_features: int

loss(target: torch.Tensor, distr_args: Tuple[torch.Tensor, ...], loc: Optional[torch.Tensor] = None, scale: Optional[torch.Tensor] = None) → torch.Tensor

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]