gen_nswn

This function allows to generate a non-stationary white noise process.

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_nswn function

<dl><dt>n_total</dt>
<dd>An <code>integer</code> indicating the length of the simulated non-stationary white noise process.</dd>
<dt>title</dt>
<dd>A <code>string</code> defining the name of the time series data.</dd>
<dt>seed</dt>
<dd>An <code>integer</code> defined for simulation replication purposes.</dd>
<dt>...</dt>
<dd>Additional parameters.</dd></dl>

Arguments

Author

Generate Non-Stationary White Noise Process — gen_nswn

<dl>

<dt>n_total</dt>
<dd>An <code>integer</code> indicating the length of the simulated non-stationary white noise process.</dd>


<dt>title</dt>
<dd>A <code>string</code> defining the name of the time series data.</dd>


<dt>seed</dt>
<dd>An <code>integer</code> defined for simulation replication purposes.</dd>


<dt>...</dt>
<dd>Additional parameters.</dd>

</dl>

Generate Non-Stationary White Noise Process

gen_nswn: Generate Non-Stationary White Noise Process

Description

Usage

Value

Arguments

Author

Examples