pytorch_forecasting.models.base_model.
AutoRegressiveBaseModelWithCovariates
Bases: pytorch_forecasting.models.base_model.BaseModelWithCovariates, pytorch_forecasting.models.base_model.AutoRegressiveBaseModel
pytorch_forecasting.models.base_model.BaseModelWithCovariates
pytorch_forecasting.models.base_model.AutoRegressiveBaseModel
Model with additional methods for autoregressive models with covariates.
Assumes the following hyperparameters:
target (str) – name of target variable
static_categoricals (List[str]) – names of static categorical variables
static_reals (List[str]) – names of static continuous variables
time_varying_categoricals_encoder (List[str]) – names of categorical variables for encoder
time_varying_categoricals_decoder (List[str]) – names of categorical variables for decoder
time_varying_reals_encoder (List[str]) – names of continuous variables for encoder
time_varying_reals_decoder (List[str]) – names of continuous variables for decoder
x_reals (List[str]) – order of continuous variables in tensor passed to forward function
x_categoricals (List[str]) – order of categorical variables in tensor passed to forward function
embedding_sizes (Dict[str, Tuple[int, int]]) – dictionary mapping categorical variables to tuple of integers where the first integer denotes the number of categorical classes and the second the embedding size
embedding_labels (Dict[str, List[str]]) – dictionary mapping (string) indices to list of categorical labels
embedding_paddings (List[str]) – names of categorical variables for which label 0 is always mapped to an embedding vector filled with zeros
categorical_groups (Dict[str, List[str]]) – dictionary of categorical variables that are grouped together and can also take multiple values simultaneously (e.g. holiday during octoberfest). They should be implemented as bag of embeddings
BaseModel for timeseries forecasting from which to inherit from
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
reduce_on_plateau_min_lr (float) – minimum learning rate for reduce on plateua learning rate scheduler. Defaults to 1e-5
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 {}.
"0"
-1
+1
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"].
lambda out: out["prediction"]
optimizer (str) – Optimizer, “ranger”, “adam” or “adamw”. Defaults to “ranger”.