aacvf

Provides functions for modeling and forecasting time series data. Forecasting is based on the innovations algorithm. A description of the innovations algorithm can be found in the textbook "Introduction to Time Series and Forecasting" by Peter J. Brockwell and Richard A. Davis. <https://link.springer.com/book/10.1007/b97391>.

George Weigt

itsmr

Time Series Analysis Using the Innovations Algorithm

aacvf function

<dl><dt>a</dt>
<dd>ARMA model</dd><dt>h</dt>
<dd>Maximum lag</dd></dl>

Arguments

Autocovariance of ARMA model — aacvf

<dl>

<dt>a</dt>
<dd>ARMA model</dd>

<dt>h</dt>
<dd>Maximum lag</dd>

</dl>

`phi`	Vector of AR coefficients (index number equals coefficient subscript)
`theta`	Vector of MA coefficients (index number equals coefficient subscript)
`sigma2`	White noise variance

aacvf: Autocovariance of ARMA model

Description

Usage

Value

Arguments

Details

See Also

Examples