BiAR {iAR} | R Documentation |
'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),
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.
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