% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/01_CiARclass.R
\name{CiAR}
\alias{CiAR}
\title{`CiAR` Class}
\usage{
CiAR(
  times = integer(0),
  series = integer(0),
  series_esd = integer(0),
  series_names = character(0),
  fitted_values = integer(0),
  kalmanlik = integer(0),
  coef = c(0.9, 0),
  tAhead = 1,
  forecast = integer(0),
  interpolated_values = integer(0),
  interpolated_times = integer(0),
  interpolated_series = integer(0),
  zero_mean = TRUE,
  standardized = TRUE,
  hessian = FALSE,
  summary = list()
)
}
\arguments{
\item{times}{A numeric vector representing the time points.}

\item{series}{A complex vector representing the values of the time series.}

\item{series_esd}{A numeric vector representing the error standard deviations of the time series.}

\item{series_names}{An optional character vector of length 1 representing the name of the series.}

\item{fitted_values}{A numeric vector containing the fitted values from the model.}

\item{kalmanlik}{A numeric value representing the Kalman likelihood of the model.}

\item{coef}{A numeric vector of length 2 containing the real (phiR) and imaginary (phiI)
parts of the autoregressive coefficient. Each value must be in [-1, 1] and the modulus
`sqrt(phiR^2 + phiI^2)` must be strictly less than 1 (stationarity condition).
Defaults to `c(0.9, 0)`. Unlike [iAR], which uses a scalar, both `CiAR` and [BiAR]
use a length-2 vector.}

\item{tAhead}{A numeric value specifying the forecast horizon (default: 1).}

\item{forecast}{A numeric vector containing the forecasted values.}

\item{interpolated_values}{A numeric vector containing the interpolated values.}

\item{interpolated_times}{A numeric vector containing the times of the interpolated data points.}

\item{interpolated_series}{A numeric vector containing the interpolated series.}

\item{zero_mean}{A logical value indicating if the model assumes a zero-mean process (default: TRUE).}

\item{standardized}{A logical value indicating if the model assumes a standardized process (default: TRUE).}

\item{hessian}{A logical value indicating whether the Hessian matrix is computed during estimation (default: FALSE).}

\item{summary}{A list containing the summary of the model fit, including diagnostics and statistical results.}
}
\description{
Represents a complex irregular autoregressive (CiAR) time series model. 
This class extends the `unidata` class and provides additional properties 
for modeling, forecasting, and interpolating irregularly observed time 
series data with both negative and positive autocorrelation.
}
\details{
The `CiAR` class is designed to handle irregularly observed time series 
data with either negative or positive autocorrelation using an autoregressive 
approach. It extends the `unidata` class to include functionalities 
specific to the `CiAR` model.

Key features of the `CiAR` class include:
- Support for irregularly observed time series data with negative 
or positive autocorrelation.
- Forecasting and interpolation functionalities for irregular time points.
- Configurable assumptions of zero-mean and standardized processes.
}
\section{Validation}{

- Inherits all validation rules from the `unidata` class:
 - `@times`, `@series`, and `@series_esd` must be numeric vectors.
 - `@times` must not contain `NA` values and must be strictly increasing.
 - The length of `@series` must match the length of `@times`.
 - The length of `@series_esd` must be 0, 1, or equal to the length of `@series`.
 - `NA` values in `@series` must correspond exactly (positionally) to `NA` values in `@series_esd`.
 - `@series_names`, if provided, must be a character vector of length 1.

- `@coef` must be a numeric vector of length 2 with no dimensions.
- Each value in `@coef` must be in the interval [-1, 1].
- The modulus `sqrt(phiR^2 + phiI^2)` must be strictly less than 1 (stationarity condition).
- `@tAhead` must be a strictly positive numeric scalar.
}

\examples{
times=gentime(n=200, distribution = "expmixture",
             lambda1 = 130, lambda2 = 6.5, p1 = 0.15, p2 = 0.85)@times
my_CiAR <- CiAR(times = times,coef = c(0.9, 0))

# Access properties
my_CiAR@coef

}
\references{
\insertRef{Elorrieta_2019}{iAR}
}
