gen_wn

Simulates a Gaussian White Noise Process with variance parameter \(\sigma ^2\).

internal

A system contains easy-to-use tools as a support for time series analysis courses. In particular, it incorporates a technique called Generalized Method of Wavelet Moments (GMWM) as well as its robust implementation for fast and robust parameter estimation of time series models which is described, for example, in Guerrier et al. (2013) <doi: 10.1080/01621459.2013.799920>. More details can also be found in the paper linked to via the URL below.

St<c3><a9>phane Guerrier

simts

Time Series Analysis Tools

Stéphane Guerrier

James Balamuta

Roberto Molinari

Justin Lee

Lionel Voirol

Yuming Zhang

Wenchao Yang

Nathanael Claussen

Yunxiang Zhang

Christian Gunning

Romain Francois

Ross Ihaka

R Core Team 

gen_wn function

<dl><dt>N</dt>
<dd>An <code>integer</code> for signal length.</dd>
<dt>sigma2</dt>
<dd>A <code>double</code> that contains process variance.</dd></dl>

Arguments

Gaussian White Noise (WN) with parameter \(\sigma^2 \in {\rm I\!R}^{+}\).
This process is defined as \(X_t\sim N(0,\sigma^2)\) and
is sometimes referred to as Angle (Velocity) Random Walk.

Process Definition

To generate the Gaussian White Noise (WN) process, we first obtain the 
standard deviation from the variance by taking a square root. Then, we 
sample \(N\) times from a \(N(0,\sigma ^2)\) distribution.

Generation Algorithm

Generate a Gaussian White Noise Process (WN(\(\sigma ^2\))) — gen_wn

<dl>

<dt>N</dt>
<dd>An <code>integer</code> for signal length.</dd>


<dt>sigma2</dt>
<dd>A <code>double</code> that contains process variance.</dd>

</dl>

To generate the Gaussian White Noise (WN) process, we first obtain the 
standard deviation from the variance by taking a square root. Then, we 
sample \(N\) times from a \(N(0,\sigma ^2)\) distribution.

gen_wn: Generate a Gaussian White Noise Process (WN(\(\sigma ^2\)))

Description

Usage

Value

Arguments

Process Definition

Generation Algorithm