ma_decomp

Trend and seasonality are modelled in a two-step approach, where first the 
trend is being estimated using moving averages and then trend + seasonality 
are being estimated using moving averages. The difference is then the 
estimated seasonality.

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

ma_decomp function

<dl><dt>yt</dt>
<dd>a time series object of class <code>"ts"</code> or an object that can be 
transformed to that class using <code><a href="/link/as.ts?package=deseats&version=1.1.0" data-mini-rdoc="deseats::as.ts">as.ts</a></code>.</dd>
<dt>k_trend</dt>
<dd>the complete absolute bandwidth (in years); represents the data 
of how many years to use around the estimation time point to consider 
for trend smoothing.</dd>
<dt>k_season</dt>
<dd>the complete absolute bandwidth (in years); represents the data 
of how many years (only from the same quarter, month, etc.) to use around the 
estimation time point for trend + seasonality 
smoothing.</dd>
<dt>season</dt>
<dd>the seasonal period in <code>yt</code>; by default, the seasonal 
period is obtained automatically from <code>yt</code>.</dd></dl>

Arguments

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

Author

Decomposition of Time Series Using Moving Averages — ma_decomp

<dl>

<dt>yt</dt>
<dd>a time series object of class <code>"ts"</code> or an object that can be 
transformed to that class using <code><a href='https://rdrr.io/r/stats/ts.html'>as.ts</a></code>.</dd>


<dt>k_trend</dt>
<dd>the complete absolute bandwidth (in years); represents the data 
of how many years to use around the estimation time point to consider 
for trend smoothing.</dd>


<dt>k_season</dt>
<dd>the complete absolute bandwidth (in years); represents the data 
of how many years (only from the same quarter, month, etc.) to use around the 
estimation time point for trend + seasonality 
smoothing.</dd>


<dt>season</dt>
<dd>the seasonal period in <code>yt</code>; by default, the seasonal 
period is obtained automatically from <code>yt</code>.</dd>

</dl>

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

ma_decomp: Decomposition of Time Series Using Moving Averages

Description

Usage

Value

Arguments

Author

Details

Examples