AutoRegressiveBaseModelWithCovariates

class pytorch_forecasting.models.base_model.AutoRegressiveBaseModelWithCovariates(log_interval: Union[int, float] = - 1, log_val_interval: Union[int, float] = None, learning_rate: Union[float, List[float]] = 0.001, log_gradient_flow: bool = False, loss: pytorch_forecasting.metrics.Metric = SMAPE(), logging_metrics: torch.nn.modules.container.ModuleList = ModuleList(), reduce_on_plateau_patience: int = 1000, weight_decay: float = 0.0, monotone_constaints: Dict[str, int] = {}, output_transformer: Callable = None, optimizer='ranger')[source]

Bases: pytorch_forecasting.models.base_model.BaseModelWithCovariates, pytorch_forecasting.models.base_model.AutoRegressiveBaseModel

BaseModel for timeseries forecasting from which to inherit from

Parameters

  • log_interval (Union[int, float], optional) – Batches after which predictions are logged. If < 1.0, will log multiple entries per batch. Defaults to -1.

  • log_val_interval (Union[int, float], optional) – batches after which predictions for validation are logged. Defaults to None/log_interval.

  • learning_rate (float, optional) – Learning rate. Defaults to 1e-3.

  • log_gradient_flow (bool) – If to log gradient flow, this takes time and should be only done to diagnose training failures. Defaults to False.

  • loss (Metric, optional) – metric to optimize. Defaults to SMAPE().

  • logging_metrics (nn.ModuleList[MultiHorizonMetric]) – list of metrics that are logged during training. Defaults to [].

  • reduce_on_plateau_patience (int) – patience after which learning rate is reduced by a factor of 10. Defaults to 1000

  • weight_decay (float) – weight decay. Defaults to 0.0.

  • monotone_constaints (Dict[str, int]) – dictionary of monotonicity constraints for continuous decoder variables mapping position (e.g. "0" for first position) to constraint (-1 for negative and +1 for positive, larger numbers add more weight to the constraint vs. the loss but are usually not necessary). This constraint significantly slows down training. Defaults to {}.

  • output_transformer (Callable) – transformer that takes network output and transforms it to prediction space. Defaults to None which is equivalent to lambda out: out["prediction"].

  • optimizer (str) – optimizer

AutoRegressiveBaseModel BaseModel