# arma_to_wv: ARMA process to WV In SMAC-Group/simts: Time Series Analysis Tools

 arma_to_wv R Documentation

## ARMA process to WV

### Description

This function computes the Haar Wavelet Variance of an ARMA process

### Usage

arma_to_wv(ar, ma, sigma2, tau)


### Arguments

 ar A vec containing the coefficients of the AR process ma A vec containing the coefficients of the MA process sigma2 A double containing the residual variance tau A vec containing the scales e.g. 2^{\tau}

### Details

The function is a generic implementation that requires a stationary theoretical autocorrelation function (ACF) and the ability to transform an ARMA(p,q) process into an MA(\infty) (e.g. infinite MA process).

### Value

A vec containing the wavelet variance of the ARMA process.

### Process Haar Wavelet Variance Formula

The Autoregressive Order p and Moving Average Order q (ARMA(p,q)) process has a Haar Wavelet Variance given by:

\frac{{{\tau _j}\left[ {1 - \rho \left( {\frac{{{\tau _j}}}{2}} \right)} \right] + 2\sum\limits_{i = 1}^{\frac{{{\tau _j}}}{2} - 1} {i\left[ {2\rho \left( {\frac{{{\tau _j}}}{2} - i} \right) - \rho \left( i \right) - \rho \left( {{\tau _j} - i} \right)} \right]} }}{{\tau _j^2}}\sigma _X^2

where \sigma _X^2 is given by the variance of the ARMA process. Furthermore, this assumes that stationarity has been achieved as it directly

SMAC-Group/simts documentation built on Sept. 4, 2023, 5:25 a.m.