gen_model

Create a time series process based on a supplied <code>ts.model</code>.

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_model function

<dl><dt>N</dt>
<dd>An <code>interger</code> containing the amount of observations for the time series.</dd>
<dt>theta</dt>
<dd>A <code>vec</code> containing the parameters to use to generate the model</dd>
<dt>desc</dt>
<dd>A <code>vector&lt;string&gt;</code> containing the different model types (AR1, WN, etc..)</dd>
<dt>objdesc</dt>
<dd>A <code>field&lt;vec&gt;</code> contains the different model objects e.g. AR1 = c(1,1)</dd></dl>

Arguments

Generate Time Series based on Model (Internal) — gen_model

<dl>

<dt>N</dt>
<dd>An <code>interger</code> containing the amount of observations for the time series.</dd>


<dt>theta</dt>
<dd>A <code>vec</code> containing the parameters to use to generate the model</dd>


<dt>desc</dt>
<dd>A <code>vector&lt;string&gt;</code> containing the different model types (AR1, WN, etc..)</dd>


<dt>objdesc</dt>
<dd>A <code>field&lt;vec&gt;</code> contains the different model objects e.g. AR1 = c(1,1)</dd>

</dl>

Generate Time Series based on Model (Internal)

gen_model: Generate Time Series based on Model (Internal)

Description

Usage

Value

Arguments