AIC.tsissm.estimate

AIC.tsissm.selection

Extract the AIC from an estimated model.

Unobserved components time series model using the linear innovations state space representation (single source of error) with choice of error distributions and option for dynamic variance. Methods for estimation using automatic differentiation, automatic model selection and ensembling, prediction, filtering, simulation and backtesting. Based on the model described in Hyndman et al (2012) <doi:10.1198/jasa.2011.tm09771>.

Alexios Ghalanos

tsissm

Linear Innovations State Space Unobserved Components Model

Alexios Galanos

AIC.tsissm.estimate function

<dl><dt>object</dt>
<dd>an object of class &#8220;tsissm.estimate&#8221;.</dd>
<dt>...</dt>
<dd>not currently used.</dd>
<dt>k</dt>
<dd>the penalty per parameter to be used; the default k = 2 is the
classical AIC.</dd></dl>

Arguments

Akaike's An Information Criterion — AIC.tsissm.estimate

<dl>

<dt>object</dt>
<dd>an object of class &#8220;tsissm.estimate&#8221;.</dd>


<dt>...</dt>
<dd>not currently used.</dd>


<dt>k</dt>
<dd>the penalty per parameter to be used; the default k = 2 is the
classical AIC.</dd>

</dl>

AIC.tsissm.estimate: Akaike's An Information Criterion

Description

Usage

Value

Arguments