auto_narima: Fitting a NARIMA Model

Description

This function fits a Neural AutoRegressive Integrated Moving Average (NARIMA) model to a univariate time series. It follows the same framework as narfima, but replaces the ARFIMA component with an ARIMA model. This function is intended for ablation studies to benchmark the performance of narfima against alternatives.

Usage

auto_narima(
  y,
  er,
  xreg = NULL,
  p,
  q,
  P = 1,
  size,
  skip,
  repeats = 1000,
  lambda = 0.5,
  lambdae = 0.5,
  scale.inputs = TRUE,
  ...
)

Value

Returns an object of class "narfima" with the same structure as narfima, but using ARIMA residuals instead of ARFIMA residuals.

Arguments

y: A numeric vector or time series of class ts.
er: A numeric vector or time series object representing the series of residuals. If missing, it will be calculated from an ARIMA model.
xreg: An optional numeric matrix of exogenous variables to be included in the model (default is NULL).
p: Integer indicating the number of lags of the input series y.
q: Integer indicating the number of lags of the errors er.
P: Integer indicating the number of seasonal lags of the input series y (default is 1).
size: Integer specifying the number of nodes of the feed-forward neural networks with a single hidden layer.
skip: Logical value indicating whether to use the direct connections in the neural network (default is TRUE).
repeats: Integer specifying the number of times to fit the neural network model (default is 1000).
lambda: Numeric value for the Box-Cox transformation parameter of y (default is 0.5).
lambdae: Numeric value for the Box-Cox transformation parameter of er (default is 0.5).
scale.inputs: Logical value indicating whether to standardize the inputs before fitting the model (default is TRUE).
...: Additional arguments passed to auto_narima.