gp_forecaster_fit_impl: GluonTS GP Forecaster Modeling Function (Bridge)
In business-science/modeltime.gluonts: 'GluonTS' Deep Learning

gp_forecaster_fit_impl

R Documentation

GluonTS GP Forecaster Modeling Function (Bridge)

Description

GluonTS GP Forecaster Modeling Function (Bridge)

Usage

gp_forecaster_fit_impl(
  x,
  y,
  freq,
  prediction_length,
  id,
  epochs = 5,
  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,
  cardinality = 1,
  kernel_output = NULL,
  params_scaling = TRUE,
  max_iter_jitter = 10,
  jitter_method = "iter",
  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).
`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)
`cardinality`	Number of time series. Default: 1
`kernel_output`	KernelOutput instance to determine which kernel subclass to be instantiated (default: RBFKernelOutput()).
`params_scaling`	Determines whether or not to scale the model parameters (default: True).
`max_iter_jitter`	Maximum number of iterations for jitter to iteratively make the matrix positive definite (default: 10).
`jitter_method`	Iteratively jitter method or use eigenvalue decomposition depending on problem size (default: "iter").
`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)
`...`	Additional parameters passed to `⁠gluonts.model.gp_forecaster.GP ForecasterEstimator()⁠`