deepar_torch_fit_impl: GluonTS DeepAR (Torch) Modeling Function (Bridge)
In business-science/modeltime.gluonts: 'GluonTS' Deep Learning

deepar_torch_fit_impl

R Documentation

GluonTS DeepAR (Torch) Modeling Function (Bridge)

Description

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()`