Represents a complex irregular autoregressive (CiAR) time series model. This class extends the `unidata` class and provides additional properties for modeling, forecasting, and interpolating irregularly observed time series data with both negative and positive autocorrelation.
CiAR(
times = integer(0),
series = integer(0),
series_esd = integer(0),
series_names = character(0),
fitted_values = integer(0),
kalmanlik = integer(0),
coef = c(0.9, 0),
tAhead = 1,
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 complex vector representing the values of the time series.
A numeric vector representing the error standard deviations of the time series.
An optional character vector of length 1 representing the name of the series.
A numeric vector containing the fitted values from the model.
A numeric value representing the Kalman likelihood of the model.
A numeric vector of length 2, containing the coefficients of the model. Each value must lie within [-1, 1]. Defaults to `c(0.9, 0)`.
A numeric value specifying the forecast horizon (default: 1).
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.
- Inherits all validation rules from the `unidata` class: - `@times`, `@series`, and `@series_esd` must be numeric vectors. - `@times` must not contain `NA` values and must be strictly increasing. - The length of `@series` must match the length of `@times`. - The length of `@series_esd` must be 0, 1, or equal to the length of `@series`. - `NA` values in `@series` must correspond exactly (positionally) to `NA` values in `@series_esd`. - `@series_names`, if provided, must be a character vector of length 1.
- `@coef` must be a numeric vector of length 2 with no dimensions. - Each value in `@coef` must be in the interval [-1, 1]. - `@tAhead` must be a strictly positive numeric scalar.
The `CiAR` class is designed to handle irregularly observed time series data with either negative or positive autocorrelation using an autoregressive approach. It extends the `unidata` class to include functionalities specific to the `CiAR` model.
Key features of the `CiAR` class include: - Support for irregularly observed time series data with negative or positive autocorrelation. - Forecasting and interpolation functionalities for irregular time points. - Configurable assumptions of zero-mean and standardized processes.
Elorrieta_2019iAR
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
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