RETAIL

<p>A <code>ts</code> object that contains the monthly total volume of retail trade 
in Germany (Index 2015 = 100)
from January 1991 
to December 2019. The object contains 348 observations.</p>

datasets

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

RETAIL function

<p>An object of class <code>ts</code> with 348 rows and 1 columns.</p>

RETAIL: Monthly Total Volume of Retail Trade in Germany

Description

Usage

Arguments

Format