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),
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
)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.
- series_names
An optional character vector representing the name of the 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.
- tAhead
A numeric value specifying the forecast horizon (default: 1).
- rho
A numeric vector containing the estimated coefficients of the model.
- 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.
Validation Rules
- `@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.
References
Elorrieta F, Eyheramendy S, Palma W, Ojeda C (2021). “A novel bivariate autoregressive model for predicting and forecasting irregularly observed time series.” Monthly Notices of the Royal Astronomical Society, 505(1), 1105-1116. ISSN 0035-8711, doi:10.1093/mnras/stab1216 , https://academic.oup.com/mnras/article-pdf/505/1/1105/38391762/stab1216.pdf.