BiAR: 'BiAR' Class

In iAR: Irregularly Observed Autoregressive Models

'BiAR' Class

Description

Represents a bivariate irregular autoregressive (BiAR) time series model. This class extends the 'multidata' class and provides additional properties for modeling, forecasting, and interpolation of bivariate time series data.

Usage

BiAR(
  times = integer(0),
  series = integer(0),
  series_esd = integer(0),
  fitted_values = integer(0),
  loglik = integer(0),
  kalmanlik = integer(0),
  coef = integer(0),
  rho = 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 numeric matrix or vector representing the values of the time series.
series_esd	A numeric matrix or vector representing the error standard deviations of the time series.
fitted_values	A numeric vector containing the fitted values from the model.
loglik	A numeric value representing the log-likelihood of the model.
kalmanlik	A numeric value representing the Kalman likelihood of the model.
coef	A numeric vector containing the estimated coefficients of the model.
rho	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 'BiAR' class is designed to handle bivariate irregularly observed time series data using an autoregressive approach. It extends the 'multidata' class to include additional properties for modeling bivariate time series.

Key features of the 'BiAR' class include: - Support for bivariate time series data. - Forecasting and interpolation functionalities for irregular time points. - Assumptions of zero-mean and standardized processes, configurable by the user. - Estimation of model parameters and likelihoods, including Kalman likelihood.

References

\insertRef

Elorrieta_2021iAR

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_BiAR <- BiAR(times = times,coef = c(0.9, 0.3), rho = 0.9)

# Access properties
my_BiAR@coef

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