GluonTS DeepAR (Torch) Modeling Function (Bridge)

Usage

deepar_torch_fit_impl(
  x,
  y,
  freq,
  prediction_length,
  id,
  epochs = 5,
  context_length = NULL,
  num_layers = 2,
  hidden_size = 40,
  num_cells = 40,
  dropout_rate = 0.1,
  num_feat_dynamic_real = 0,
  num_feat_static_cat = 0,
  num_feat_static_real = 0,
  cardinality = NULL,
  embedding_dimension = NULL,
  distr_output = "default",
  scaling = TRUE,
  lags_seq = NULL,
  time_features = NULL,
  num_parallel_samples = 100,
  batch_size = 32,
  scale_by_id = FALSE,
  ...
)

Arguments

x: A dataframe of xreg (exogenous regressors)
y: A numeric vector of values to fit
freq: A pandas timeseries frequency such as "5min" for 5-minutes or "D" for daily. Refer to Pandas Offset Aliases.
prediction_length: Numeric value indicating the length of the prediction horizon
id: A quoted column name that tracks the GluonTS FieldName "item_id"
epochs: Number of epochs that the network will train (default: 5).
context_length: Number of steps to unroll the RNN for before computing predictions (default: NULL, in which case context_length = prediction_length)
num_layers: Number of RNN layers (default: 2)
hidden_size: Hidden units
num_cells: Number of RNN cells for each layer (default: 40)
dropout_rate: Dropout regularization parameter (default: 0.1)
num_feat_dynamic_real: Number of dynamic numeric features
num_feat_static_cat: Number of static categorical features
num_feat_static_real: Number of static numeric features
cardinality: Number of values of each categorical feature. This must be set if use_feat_static_cat == TRUE (default: NULL)
embedding_dimension: Dimension of the embeddings for categorical features (default: min(50, (cat+1)//2) for cat in cardinality)
distr_output: Distribution to use to evaluate observations and sample predictions (default: StudentTOutput())
scaling: Whether to automatically scale the target values (default: TRUE)
lags_seq: Indices of the lagged target values to use as inputs of the RNN (default: NULL, in which case these are automatically determined based on freq)
time_features: Time features to use as inputs of the RNN (default: None, in which case these are automatically determined based on freq)
num_parallel_samples: Number of evaluation samples per time series to increase parallelism during inference. This is a model optimization that does not affect the accuracy (default: 100)
batch_size: Number of examples in each batch (default: 32).
scale_by_id: Scales numeric data by id group using mean = 0, standard deviation = 1 transformation. (default: FALSE)
...: Parameters passed to pytorch_lightning.trainer.Trainer()