grouped_arma: Fit grouped ARMA model to time series
In MichaelHoerner/groupedtseries: Analysis of grouped time series

Fit an ARMA model with the order c(p,q) to a group of univariate time series by finding coefficients that serve best the whole group.

grouped_arma(
  x,
  order = c(1, 1),
  lag = NULL,
  coef = NULL,
  include.intercept = TRUE,
  series = NULL,
  qr.tol = 1e-07,
  quick.estimation = FALSE,
  min_seg_length = NULL,
  method = "ML",
  ...
)

`x`	a two-dimensional numeric vector of a grouped time series with accompanying group index in second dimension. One-dimensional if input shall be only a single time series.
`order`	a vector c(p,q) that indicates the order p of the AR part and q of the MA part
`lag`	a list with components `ar` and `ma`. Each component is an integer vector, specifying the AR and MA lags that are included in the model. If both, `order` and `lag`, are given, only the specification from `lag` is used.
`coef`	If given this numeric vector is used as the initial estimate of the ARMA coefficients. The preliminary estimator suggested in Hannan and Rissanen (1982) is used for the default initialization.
`include.intercept`	Should the model contain an intercept?
`series`	name for the series. Defaults to `deparse(substitute(x))`.
`qr.tol`	the `tol` argument for `qr` when computing the asymptotic standard errors of `coef`.
`quick.estimation`	logical value (TRUE, FALSE) indicating whether a quick estimation mode shall be used
`...`	additional arguments for `optim` when fitting the model.

Object of class arma.

`lag`	the lag specification of the fitted model.
`coef`	estimated ARMA coefficients for the fitted model across all grouped time series.
`css`	the conditional sum-of-squared errors.
`n.used`	the number of observations of `x`.
`residuals`	the series of residuals.
`fitted.values`	the fitted series.
`series`	the name of the series `x`.
`frequency`	the frequency of the series `x`.
`call`	the call of the `arma` function.
`vcov`	estimate of the asymptotic-theory covariance matrix for the coefficient estimates.
`convergence`	The `convergence` integer code from `optim`.
`include.intercept`	Does the model contain an intercept?