ts_pmm2: Fit a time series model using the PMM2 method

Description

Fit a time series model using the PMM2 method

Usage

ts_pmm2(
  x,
  order,
  model_type = c("ar", "ma", "arma", "arima"),
  method = "pmm2",
  max_iter = 50,
  tol = 1e-06,
  include.mean = TRUE,
  initial = NULL,
  na.action = na.fail,
  regularize = TRUE,
  reg_lambda = 1e-08,
  verbose = FALSE
)

Value

An S4 object TS2fit of the corresponding subclass

Arguments

x: Numeric vector of time series data
order: Model order specification: - For AR models: a single integer (AR order) - For MA models: a single integer (MA order) - For ARMA models: vector c(p, q) (AR and MA orders) - For ARIMA models: vector c(p, d, q) (AR, differencing, and MA orders)
model_type: String specifying the model type: "ar", "ma", "arma", or "arima"
method: String: estimation method, one of "pmm2" (default), "css", "ml", "yw", "ols"
max_iter: Integer: maximum number of iterations for the algorithm
tol: Numeric: tolerance for convergence
include.mean: Logical: whether to include a mean (intercept) term
initial: List or vector of initial parameter estimates (optional)
na.action: Function for handling missing values, default is na.fail
regularize: Logical, add small values to diagonal for numerical stability
reg_lambda: Regularization parameter (if regularize=TRUE)
verbose: Logical: whether to print progress information

Details

The PMM2 algorithm works as follows:

Fits an initial model using a standard method (OLS, Yule-Walker, CSS or ML)
Computes central moments (m2, m3, m4) from initial residuals/innovations
Uses these moments with a specialized solver (pmm2_algorithm) to find robust parameter estimates