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.
Usage
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
)Arguments
- times
A numeric vector representing the time points.
- series
A complex vector representing the values of the time series.
- series_esd
A numeric vector representing the error standard deviations of the time series.
- series_names
An optional character vector of length 1 representing the name of the series.
- fitted_values
A numeric vector containing the fitted values from the model.
- kalmanlik
A numeric value representing the Kalman likelihood of the model.
- coef
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)`.
- 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 `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.
Validation
- 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.
References
Elorrieta, F, Eyheramendy, S, Palma, W (2019). “Discrete-time autoregressive model for unequally spaced time-series observations.” A&A, 627, A120. doi:10.1051/0004-6361/201935560 .