Data-Driven Locally Weighted Regression for Trend and Seasonality in TS
Printing of deseats Function Results
Animate Locally Weighted Regression Results
Automatic Creation of Animations
AR Representation of an ARMA Model
MA Representation of an ARMA Model
Plot Method for Decomposition Results in the Style of ggplot2
ggplot2 Plot Method for Class "deseats_fc"
ggplot2 Plot Method for the Results of a Hamilton Filter
Trend and Seasonality Estimation Using the Berlin Procedure 4.1
Bootstrapping Confidence Intervals for Locally Weighted Regression Ban...
Show Method for Objects of Class "s_semiarma"
Retrieve the Used Bandwidth from an Estimation Object
Create Gain Function from a Linear Time Series Filter
Deseasonalize Time Series
Locally Weighted Regression for Trend and Seasonality in Equidistant T...
Exponentiate deseats Forecasts
Automatic Creation of Animations
Fitted Components of the Hamilton Filter
Obtain gain function values for DeSeaTS Trend and Detrend Filters
Gain Function Generic
Calculation of Theoretically Optimal Bandwidth and Its Components
Time Series Filtering Using the Hamilton Filter
Decomposition of Time Series Using Local Linear Regression
Decomposition of Time Series Using Linear Regression
Decomposition of Time Series Using Moving Averages
Forecasting Accuracy Measure Calculation
Retrieve or Set Smoothing Options
Smoothing Option Generics
Plot Method for Decomposition Results in the Style of Base R Plots
Plot Method for Class "deseats_fc"
Plot Method for the Results of a Hamilton Filter
Point and Interval Forecasts for Seasonal Semi-ARMA Models
Show Method for Smoothing Options
Read in a Dataset Directly as an Object of Class "ts" or "mts"
Shiny App for Decomposing Seasonal Time Series
Fitting of a Seasonal Semiparametric ARMA Model
Creation of Seasonal Plots in the Style of ggplot2
Creation of Seasonal Plots
Optimal Bandwidth Estimation for Locally Weighted Regression in Equidi...
Specification of Smoothing Options
Obtain Individual Components of a Decomposed Time Series
Obtain Estimated Components of a Time Series
Time Series Object Conversion from "zoo" to "ts"
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.