nnetar_fit_impl: Low-Level NNETAR function for translating modeltime to...

In modeltime: The Tidymodels Extension for Time Series Modeling

Low-Level NNETAR function for translating modeltime to forecast

Description

Low-Level NNETAR function for translating modeltime to forecast

Usage

nnetar_fit_impl(
  x,
  y,
  period = "auto",
  p = 1,
  P = 1,
  size = 10,
  repeats = 20,
  decay = 0,
  maxit = 100,
  ...
)

Arguments

x	A dataframe of xreg (exogenous regressors)
y	A numeric vector of values to fit
period	A seasonal frequency. Uses "auto" by default. A character phrase of "auto" or time-based phrase of "2 weeks" can be used if a date or date-time variable is provided.
p	Embedding dimension for non-seasonal time series. Number of non-seasonal lags used as inputs. For non-seasonal time series, the default is the optimal number of lags (according to the AIC) for a linear AR(p) model. For seasonal time series, the same method is used but applied to seasonally adjusted data (from an stl decomposition). If set to zero to indicate that no non-seasonal lags should be included, then P must be at least 1 and a model with only seasonal lags will be fit.
P	Number of seasonal lags used as inputs.
size	Number of nodes in the hidden layer. Default is half of the number of input nodes (including external regressors, if given) plus 1.
repeats	Number of networks to fit with different random starting weights. These are then averaged when producing forecasts.
decay	Parameter for weight decay. Default 0.
maxit	Maximum number of iterations. Default 100.
...	Additional arguments passed to forecast::nnetar

modeltime documentation built on Oct. 23, 2024, 1:07 a.m.