deepar_fit_impl: GluonTS DeepAR Modeling Function (Bridge)
In modeltime.gluonts: 'GluonTS' Deep Learning

GluonTS DeepAR Modeling Function (Bridge)

deepar_fit_impl(
  x,
  y,
  freq,
  prediction_length,
  id,
  epochs = 5,
  batch_size = 32,
  num_batches_per_epoch = 50,
  learning_rate = 0.001,
  learning_rate_decay_factor = 0.5,
  patience = 10,
  minimum_learning_rate = 5e-05,
  clip_gradient = 10,
  weight_decay = 1e-08,
  init = "xavier",
  ctx = NULL,
  hybridize = TRUE,
  context_length = NULL,
  num_layers = 2,
  num_cells = 40,
  cell_type = "lstm",
  dropout_rate = 0.1,
  use_feat_dynamic_real = FALSE,
  use_feat_static_cat = FALSE,
  use_feat_static_real = FALSE,
  cardinality = NULL,
  embedding_dimension = NULL,
  distr_output = "default",
  scaling = TRUE,
  lags_seq = NULL,
  time_features = NULL,
  num_parallel_samples = 100
)

`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).
`batch_size`	Number of examples in each batch (default: 32).
`num_batches_per_epoch`	Number of batches at each epoch (default: 50).
`learning_rate`	Initial learning rate (default: 10-3 ).
`learning_rate_decay_factor`	Factor (between 0 and 1) by which to decrease the learning rate (default: 0.5).
`patience`	The patience to observe before reducing the learning rate, nonnegative integer (default: 10).
`minimum_learning_rate`	Lower bound for the learning rate (default: 5x10-5 ).
`clip_gradient`	Maximum value of gradient. The gradient is clipped if it is too large (default: 10).
`weight_decay`	The weight decay (or L2 regularization) coefficient. Modifies objective by adding a penalty for having large weights (default 10-8 ).
`init`	Initializer of the weights of the network (default: “xavier”).
`ctx`	The mxnet CPU/GPU context. Refer to using CPU/GPU in the mxnet documentation. (default: NULL, uses CPU)
`hybridize`	Increases efficiency by using symbolic programming. (default: TRUE)
`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)
`num_cells`	Number of RNN cells for each layer (default: 40)
`cell_type`	Type of recurrent cells to use (available: 'lstm' or 'gru'; default: 'lstm')
`dropout_rate`	Dropout regularization parameter (default: 0.1)
`use_feat_dynamic_real`	Whether to use the 'feat_dynamic_real' field from the data (default: FALSE)
`use_feat_static_cat`	Whether to use the feat_static_cat field from the data (default: FALSE)
`use_feat_static_real`	Whether to use the feat_static_real field from the data (default: FALSE)
`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)