deseats-package {deseats}

Deseasonalize Time Series

Description

A library of decomposition methods for equidistant time series with trend and seasonality.

Details

deseats is an R package for the decomposition of equidistant time series with trend and seasonality. First and foremost, an own algorithm for bandwidth selection in locally weighted regression of such time series (with short-range dependence) is implemented that is based on both the algorithms by Feng (2013) and Feng et al. (2020). For comparison, a simplified version of the BV4.1 (Berlin Procedure 4.1, Speth, 2004), is implemented as well that allows to implement the BV4.1 base model (trend component + seasonality component + irregular component) without any of the additional BV4.1 components (such as the calendar component). Permission to include the BV4.1 base model procedure was kindly provided by the Federal Statistical Office of Germany.

Main Functions

The main functions of the package are:

deseats:

locally weighted regression with automatically selected bandwidth for decomposition,

BV4.1:

BV4.1 base model for decomposition,

lm_decomp:

ordinary least squares for decomposition,

llin_decomp:

local linear regression for decomposition,

ma_decomp:

moving averages for decomposition,

hamilton_filter:

the time series filter by Hamilton.

Datasets

The package includes a few datasets. Follow the corresponding links to the documentation of the datasets to find additional information including the sources.

CIVLABOR:

civilian labor force level in the USA.

CONSUMPTION:

real final consumption expenditure for Australia.

COVID:

new COVID-19 cases in Germany.

DEATHS:

recorded number of deaths in Germany.

ENERGY:

production and distribution of electricity, gas, steam and air conditioning in Germany.

EXPENDITURES:

consumption expenditures in the USA.

GDP:

GDP of the USA.

HOUSES:

new one family houses sold in the USA.

LIVEBIRTHS:

recorded number of livebirths in Germany.

NOLABORFORCE:

number of persons in the USA not belonging to the labor force.

RAINFALL:

average amount of rain in Germany.

RETAIL:

Retail sale volume in Germany.

SAVINGS:

savings of private households in Germany.

SUNSHINE:

average hours of sunshine in Germany.

TEMPERATURE:

average temperature in Germany.

License

The package is distributed under the General Public License v3 ([GPL-3](https://tldrlegal.com/license/gnu-general-public-license-v3-(gpl-3))).

Author(s)

• Dominik Schulz (Research Assistant) (Department of Economics, Paderborn University),
  Author and Package Creator

• Yuanhua Feng (Department of Economics, Paderborn University),
  Author

References

• Feng, Y. (2013). An iterative plug-in algorithm for decomposing seasonal time series using the Berlin Method. Journal of Applied Statistics, 40(2): 266-281. DOI: 10.1080/02664763.2012.740626.

• Feng, Y., Gries. T, and Fritz, M. (2020). Data-driven local polynomial for the trend and its derivatives in economic time series. Journal of Nonparametric Statistics, 32(2): 510-533. DOI: 10.1080/10485252.2020.1759598.

• Speth, H.-T. (2004). Komponentenzerlegung und Saisonbereinigung ökonomischer Zeitreihen mit dem Verfahren BV4.1. Methodenberichte 3. Statistisches Bundesamt. URL: https://www.destatis.de/DE/Methoden/Saisonbereinigung/BV41-methodenbericht-Heft3_2004.pdf?__blob=publicationFile.