CiAR: 'CiAR' Class

In iAR: Irregularly Observed Autoregressive Models

'CiAR' Class

Description

Represents a complex irregular autoregressive (CiAR) time series model. This class extends the 'unidata' class and provides additional properties for modeling, forecasting, and interpolation of complex-valued time series data.

Usage

CiAR(
  times = integer(0),
  series = integer(0),
  series_esd = integer(0),
  fitted_values = integer(0),
  kalmanlik = integer(0),
  coef = integer(0),
  tAhead = 1,
  forecast = integer(0),
  interpolated_values = integer(0),
  interpolated_times = integer(0),
  interpolated_series = integer(0),
  zero_mean = TRUE,
  standardized = TRUE
)

Arguments

times	A numeric vector representing the time points.
series	A complex vector representing the values of the time series.
series_esd	A numeric vector representing the error standard deviations of the time series.
fitted_values	A numeric vector containing the fitted values from the model.
kalmanlik	A numeric value representing the Kalman likelihood of the model.
coef	A numeric vector containing the estimated coefficients of the model.
tAhead	A numeric value specifying the forecast horizon (default: 1).
forecast	A numeric vector containing the forecasted values.
interpolated_values	A numeric vector containing the interpolated values.
interpolated_times	A numeric vector containing the times of the interpolated data points.
interpolated_series	A numeric vector containing the interpolated series.
zero_mean	A logical value indicating if the model assumes a zero-mean process (default: TRUE).
standardized	A logical value indicating if the model assumes a standardized process (default: TRUE).

Details

The 'CiAR' class is designed to handle irregularly observed, complex-valued time series data using an autoregressive approach. It extends the 'unidata' class to include functionalities specific to complex-valued data.

Key features of the 'CiAR' class include: - Support for complex-valued time series data. - Forecasting and interpolation functionalities for irregular time points. - Assumptions of zero-mean and standardized processes, configurable by the user.

References

\insertRef

Elorrieta_2019iAR

Examples

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

# Access properties
my_CiAR@coef

iAR documentation built on April 4, 2025, 2:21 a.m.