Function estimates Prediction Likelihood Score for the provided model

Functions implementing Single Source of Error state space models for purposes of time series analysis and forecasting.
The package includes ADAM (Svetunkov, 2023, <https://openforecast.org/adam/>),
Exponential Smoothing (Hyndman et al., 2008, <doi: 10.1007/978-3-540-71918-2>),
SARIMA (Svetunkov & Boylan, 2019 <doi: 10.1080/00207543.2019.1600764>),
Complex Exponential Smoothing (Svetunkov & Kourentzes, 2018, <doi: 10.13140/RG.2.2.24986.29123>),
Simple Moving Average (Svetunkov & Petropoulos, 2018 <doi: 10.1080/00207543.2017.1380326>)
and several simulation functions. It also allows dealing with intermittent demand based on the
iETS framework (Svetunkov & Boylan, 2019, <doi: 10.13140/RG.2.2.35897.06242>).

Ivan Svetunkov

smooth

Forecasting Using State Space Models

pls function

Prediction Likelihood Score — pls

<dl>

<dt>object</dt>
<dd>The model estimated using smooth functions. This thing also accepts
other models (e.g. estimated using functions from forecast package), but may not always
work properly with them.</dd>


<dt>holdout</dt>
<dd>The values for the holdout part of the sample. If the model was fitted
on the data with the <code>holdout=TRUE</code>, then the parameter is not needed.</dd>


<dt>...</dt>
<dd>Parameters passed to multicov function. The function is called in order to get
the covariance matrix of 1 to h steps ahead forecast errors.</dd>

</dl>

Arguments

Ivan Svetunkov, <a href='mailto:ivan@svetunkov.com'>ivan@svetunkov.com</a>

pls: Prediction Likelihood Score

Description

Usage

Arguments

Value

Details

References

Examples