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.
BiAR(
times = integer(0),
series = integer(0),
series_esd = integer(0),
series_names = character(0),
fitted_values = integer(0),
loglik = integer(0),
kalmanlik = integer(0),
coef = c(0.8, 0),
tAhead = 1,
rho = 0,
forecast = integer(0),
interpolated_values = integer(0),
interpolated_times = integer(0),
interpolated_series = integer(0),
zero_mean = TRUE,
standardized = TRUE,
hessian = FALSE,
summary = list()
)A numeric vector representing the time points.
A numeric matrix or vector representing the values of the time series.
A numeric matrix or vector representing the error standard deviations of the time series.
An optional character vector representing the name of the series.
A numeric vector containing the fitted values from the model.
A numeric value representing the log-likelihood of the model.
A numeric value representing the Kalman likelihood of the model.
A numeric vector containing the estimated coefficients of the model.
A numeric value specifying the forecast horizon (default: 1).
A numeric vector containing the estimated coefficients of the model.
A numeric vector containing the forecasted values.
A numeric vector containing the interpolated values.
A numeric vector containing the times of the interpolated data points.
A numeric vector containing the interpolated series.
A logical value indicating if the model assumes a zero-mean process (default: TRUE).
A logical value indicating if the model assumes a standardized process (default: TRUE).
A logical value indicating whether the Hessian matrix is computed during estimation (default: FALSE).
A list containing the summary of the model fit, including diagnostics and statistical results.
- `@times` must be a numeric vector without dimensions and strictly increasing. - `@series` must be a numeric matrix with two columns (bivariate) or be empty. - The number of rows in `@series` must match the length of `@times`. - `@series_esd`, if provided, must be a numeric matrix. Its dimensions must match those of `@series`, or it must have one row and the same number of columns. - If `@series_esd` contains NA values, they must correspond positionally to NA values in `@series`. - `@series_names`, if provided, must be a character vector with length equal to the number of columns in `@series`, and all names must be unique. - `@coef` must be a numeric vector of length 2, with each element strictly between -1 and 1. - `@tAhead` must be a strictly positive numeric scalar.
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.
Elorrieta_2021iAR
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
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