CiAR {iAR}R Documentation

'CiAR' Class

Description

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.

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_CiAR <- CiAR(times = times,coef = c(0.9, 0))

# Access properties
my_CiAR@coef


[Package iAR version 1.3.1 Index]