gluonts.torch.model.mqf2.lightning_module module

class gluonts.torch.model.mqf2.lightning_module.MQF2MultiHorizonLightningModule(model_kwargs: dict, lr: float = 0.001, weight_decay: float = 1e-08, patience: int = 10)

Bases: LightningModule

LightningModule class for the model MQF2 proposed in the paper Multivariate Quantile Function Forecaster by Kan, Aubet, Januschowski, Park, Benidis, Ruthotto, Gasthaus.

This is the multi-horizon (multivariate in time step) variant of MQF2

This class is based on gluonts.torch.model.deepar.lightning_module.DeepARLightningModule

Parameters:

• model_kwargs – Keyword arguments to construct the MQF2MultiHorizonModel to be trained.

• loss – Distribution loss.

• lr – Learning rate.

• weight_decay – Weight decay during training.

• patience – Patience parameter for learning rate scheduler, default: 10.

configure_optimizers()

Returns the optimizer to use.

forward(*args, **kwargs)

Same as torch.nn.Module.forward().

Parameters:

• *args – Whatever you decide to pass into the forward method.

• **kwargs – Keyword arguments are also possible.

Returns:

Your model’s output

training_step(batch, batch_idx: int)

Execute training step.

validation_step(batch, batch_idx: int)

Execute validation step.