ardec: Time series autoregressive decomposition

In ArDec: Time Series Autoregressive-Based Decomposition

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)

 

ArDec documentation built on June 1, 2022, 1:06 a.m.