show,s_semiarma-method

Print results of the function <code>s_semiarma</code> to the console.

Various methods for the identification of trend and seasonal
components in time series (TS) are provided. Among them is a data-driven locally
weighted regression approach with automatically selected bandwidth for
equidistant short-memory time series. The approach is a
combination / extension of the algorithms by
Feng (2013) <doi:10.1080/02664763.2012.740626> and Feng, Y., Gries, T.,
and Fritz, M. (2020) <doi:10.1080/10485252.2020.1759598> and a brief
description of this new method is provided in the package documentation.
Furthermore, the package allows its users to apply the base model of the
Berlin procedure, version 4.1, as described in Speth (2004) <https://www.destatis.de/DE/Methoden/Saisonbereinigung/BV41-methodenbericht-Heft3_2004.pdf?__blob=publicationFile>.
Permission to include this procedure was kindly provided by the Federal
Statistical Office of Germany.

Dominik Schulz

deseats

Data-Driven Locally Weighted Regression for Trend and
Seasonality in TS

Yuanhua Feng

show,s_semiarma-method function

<dl><dt>object</dt>
<dd>an object of class <code>"s_semiarma"</code>.</dd></dl>

Arguments

<ul>
<li>Dominik Schulz (Research Assistant) (Department of Economics, Paderborn
University), 
Author and Package Creator</li>
</ul>

Author

Show Method for Objects of Class "s_semiarma" — show,s_semiarma-method

<dl>

<dt>object</dt>
<dd>an object of class <code>"s_semiarma"</code>.</dd>

</dl>

<ul>
<li>Dominik Schulz (Research Assistant) (Department of Economics, Paderborn
University), 
Author and Package Creator</li>
</ul>

State of Data and AI Literacy Report 2025

show,s_semiarma-method: Show Method for Objects of Class `"s_semiarma"`

Description

Usage

Value

Arguments

Author

Details