| ardec {ArDec} | R Documentation |
Time series autoregressive decomposition
Description
Decomposition of a time series into latent subseries from a fitted autoregressive model
Usage
ardec(x, coef, ...)
Arguments
x |
time series |
coef |
autoregressive parameters of AR(p) model |
... |
additional arguments for specific methods |
Details
If an observed time series can be adequately described by an (eventually high order) autoregressive AR(p) process, a constructive result (West, 1997) yields a time series decomposition in terms of latent components following either AR(1) or AR(2) processes depending on the eigenvalues of the state evolution matrix.
Complex eigenvalues r exp(iw) correspond to pseudo-periodic oscillations as a damped sine wave with fixed period (2pi/w) and damping factor r. Real eigenvalues correspond to a first order autoregressive process with parameter r.
Value
A list with components:
period |
periods of latent components |
modulus |
damping factors of latent components |
comps |
matrix of latent components |
Author(s)
S. M. Barbosa
References
West, M. (1997), Time series decomposition. Biometrika, 84, 489-494.
West, M. and Harrisson, P.J. (1997), Bayesian Forecasting and Dynamic Models, Springer-Verlag.
Examples
data(tempEng)
coef=ardec.lm(tempEng)$coefficients
# warning: running the next command can be time comsuming!
decomposition=ardec(tempEng,coef)